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World World World MalariaMalariaMalaria Report 2008 Report 2008 Report 2008

World malaria report2008

© World Health Organization 2008

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Cover design by Anne Guilloux and Christopher Dye. The bars show the growing number of insecticide-treated nets (ITN) sold or delivered worldwide between 2001 and 2006. There was a modest increase in the number of conventional ITN delivered in 2005 and 2006 (blue bars), and a large increase in the number of long-lasting insecticidal nets delivered (orange bars), especially to countries in the African Region (Chapter 4).

Printed in Switzerland

WHO Library Cataloguing-in-Publication Data

World malaria report 2008.

“WHO/HTM/GMP/2008.1”.

1.Malaria – prevention and control. 2.Malaria – drug therapy. 3.Antimalarials. 4.National health programmes. 5.Statistics. I.World Health Organization.

ISBN 978 92 4 156369 7 (NLM classification: WC 765)

WORLD MALARIA REPORT 2008 iii

…Contents

Abbreviations ivAcknowledgements v

Summary viiKey points viiiRésumé xi Points essentiels xiiResumen xviPuntos clave xvii

1. Introduction 12. Policies, strategies and targets for malaria control 33. Estimated burden of malaria in 2006 94. Interventions to control malaria 165. Impact of malaria control 27

Profiles: 30 high-burden countries 33

Annex 1 Estimating the numbers of malaria cases and deaths by country in 2006 129Annex 2 Estimated and reported cases and deaths, 2006 141Annex 3 A. Reported malaria cases, 1990–2007 145 B. Reported malaria deaths, 1990–2007 149Annex 4 A. Recommended policies and strategies for malaria control, 2007 151 B. Antimalarial drug policy, 2008 156Annex 5 Operational coverage of insecticide-treated nets, indoor residual spraying and antimalarial treatment, 2004–2007 159Annex 6 A. Household surveys of mosquito net ownership and usage, 2001–2007 169 B. Household surveys of antimalarial treatment, 2001–2007 177Annex 7 Funding for malaria control, 2004–2007 183

iv WORLD MALARIA REPORT 2008

…Abbreviations

ACT Artemisinin-based combination therapy

AIDS Acquired immunodeficiency syndrome

AFR WHO African Region

AFRO WHO Regional Office for Africa

AMR WHO Region of the Americas

AMRO WHO Regional Office for the Americas

API Annual parasite incidence

DDT Dichloro-diphenyl-trichloroethane

DHS Demographic household survey

EMR WHO Eastern Mediterranean Region

EMRO WHO Regional Office for the Eastern Mediterranean

EUR WHO European Region

EURO WHO Regional Office for Europe

GBD Global burden of diseases

GMP Global Malaria Programme

HIV Human immunodeficiency virus

IAEG Inter-Agency and Expert Group on MDG Indicators

IRS Indoor residual spraying

IPT Intermittent preventive treatment

ITN Insecticide-treated nets

LLIN Long-lasting insecticidal nets

MDGs Millennium Development Goals

MERG Monitoring and Evaluation Reference Group (for malaria)

MICS Multiple indicator cluster survey

MIS Malaria indicator survey

NMCP National malaria control programme

PAHO Pan-American Health Organization

RBM Roll Back Malaria

RDT Rapid diagnostic test

SEAR WHO South-East Asia Region

SEARO WHO Regional Office for South-East Asia

SPR Slide positivity rate

SUFI Scaling Up for Impact

WPR WHO Western Pacific Region

WPRO WHO Regional Office for the Western Pacific

Abbreviations of antimalarial treatmentsAQ Amodiaquine

AL Artemether-lumefantrine

AM Artemether

AS Artesunate

C Clindamycine

CQ Chloroquine

D Doxycycline

IPT Intermittent preventive treatment

MQ Mefloquine

PG Proguanil

PIP Piperaquine

PQ Primaquine

QN Quinine

SP Sulfadoxine-pyrimethamine

T Tetracycline

(d) Days on treatment course

World Malaria report 2008 v

…Acknowledgements

the World malaria report 2008 was written and produced by Maru aregawi, richard Cibulskis, Mac otten, ryan Williams and Christopher dye, on behalf of the WHo Global Malaria programme.

Maru aregawi, richard Cibulskis and ryan Williams designed the data-collection form, and compiled and reviewed data provided by national malaria control programmes. ryan Williams designed and managed the global malaria data-base, automated the production of country profiles, and prepared maps and annexes 2–7 with the assistance of anne Guilloux. the analysis of policies and strategies for malaria control, and of epidemiological and financial data, was carried out by Maru aregawi, richard Cibulskis, Chris-topher dye, Mac otten and ryan Williams.

additional information on the adoption and implementa-tion of WHo policies and strategies for malaria control was provided by: Medhin ambachew, amy Barrette, andrea Bosman, Valentina Buj, Kabir Cham, pierre Guillet, Stefan Hoyer, Kamini Mendis, Sivakumaran Murugasampillay, peter olumese, aafje rietveld, pascal ringwald, Silvia Schwarte, Sergio Spinaci, Marian Warsame and Wilson Were of the WHo Global Malaria programme.

richard Cibulskis developed methods and undertook the analysis of the burden of malaria with the support of Maru aregawi, Christopher dye, Mac otten and ryan Williams. Colin Mathers played an essential role in validating estima-tion methods and in aligning the estimates of malaria cases and deaths with the Global Burden of disease project. eline Korenromp (Global Fund to fight aidS, tuberculosis and Malaria) and other colleagues in the morbidity task force of the roll Back Malaria Monitoring and evaluation reference Group reviewed the estimation methods (annex 1).

data from the demographic household survey (dHS), multi-ple indicator cluster survey (MiCS) and malaria indicator sur-vey (MiS) platforms (annex 6) were compiled and checked by Fred arnold (dHS), erin eckert (Macro international inc.), emily White Johansson (United Nations Children’s Fund, UNiCeF) and tessa Wardlaw (UNiCeF), together with WHo.

We also thank adam Wolkon (US Centers for disease Control and prevention, CdC), amy ratcliffe (CdC), allen Hightow-er (CdC) and rick Steketee (Malaria Control and evaluation partnership in africa) who reviewed information on indica-tors, targets, and on malaria in the WHo african region.

The following WHO staff in regional and subregional offices assisted with the collection and validation of data, and reviewed epidemio-logical estimates and country profiles: etienne Magloire Mink-oulou (aFro), Khoti Gausi (aFro/eastern and southern inter-Country Support team, iCSt), Samson Katikiti (aFro/eastern and southern iCSt), Nathan Bakyaita (aFro), ama-dou Bailo diallo (aFro/central iSt), abderrahmane Kharchi (aFro/western iSt), Jean-olivier Guintran (aFro/western iSt),Georges alfred Ki-zerbo (aFro), Soce Fall (aFro), Ghasem Zamani (eMro), Hoda atta (eMro), Karen taksøe-Vester (eUro), Mikhail ejov (eUro), Keith Carter (aMro), rainier escalada (aMro), eva-Maria Christophel, Violeta Gonzales (Wpro), Krongthong thimasarn (Searo), r. ras-togi (Searo).

The following played a key role in collecting and reviewing national data from the malaria-endemic countries: ahmad Walid Sediqi (afghanistan); lic pablo orellano (argentina); ara Kesh-ishyan (armenia); Viktor Gasimov (azerbaijan); a. Mannan Bangali (Bangladesh); Kossou Hortense (Benin); dechen pemo (Bhutan); Kentse Moakofhi, t. Mosweunyane (Bot-swana); lakshmi devi telsinghe (Brunei darussalam); Jean eric ouedraogo (Burkina Faso); Baza dismas (Burundi); duong Socheat, Md abdur rashid (Cambodia); Kwake Simon Fozo (Cameroon); antónio Moreira (Cape Verde); Max roger Koula (Central african republic); issa donan-Gouni, dap-sou Guidinassou (Chad); Yi Wang (China); ibrahima aha-mada (Comoros); Bouka roger Germain (Congo); Côme donatien K. Feby angui (Côte d’ivoire); p. Vason (demo-cratic people’s republic of Korea); Kanyeba Mpiana, Godée Hedwige (democratic republic of the Congo); Johanes don Bosco (democratic republic of timor-leste); Jaime enrique alleman escobar (el Salvador); tewolde Ghebremeskel (eri-trea); ambachew Medhin Yohannes, Worku Bekele (ethio-pia); pambou Nicaise (Gabon); Malang Fofana, Mamo Jawla (Gambia); Merab iosava (Georgia); Constance Barte-plange (Ghana); amadou Sadio diallo (Guinea); evangelino albano Quade (Guinea-Bissau); laura Julia Salgado, Y. ricardo Kafie (Honduras); Charles tobing (indonesia); ahmad raei-si (islamic republic of iran); ahmed akram ahmed (iraq); Zhanna Shapiyeva (Kazakhstan); r.J. Kiptui (Kenya); Nur-bolot Usenbaev (Kyrgyzstan); deyer Gopinath (lao peo-ple’s democratic republic); tolbert G. Nyenswah (liberia); Benjamin ramarosandratana (Madagascar); Storn Kabu-luzi (Malawi); Che abdullah Hassan (Malaysia); Hassan Samir (Maldives); ignace traoroe (Mali); Sid M’Hamed ould

vi WORLD MALARIA REPORT 2008

Lebatt (Mauritania); Than Winn (Myanmar); S.T. Katokele (Namibia); Soe Aung (Nepal); Francisco Acevedo, Julio Rosales, Aída Soto (Nicaragua); Abani Maazou (Niger); T.O. Sofola (Nigeria); Faisal Mansoor (Pakistan); Sharon Jamea, Kwabena Larbi, Leo Makita (Papua New Guinea); Cristy Galang, Raman Velayudhan (Philippines); Hye Kyung Park (Republic of Korea); Corine Karema (Rwanda); José Alvaro Leal Duarte (Sao Tome and Principe); M.H. Alza-hrani (Saudi Arabia); Mame Birame Diof, Médoune Diop (Senegal); Samuel H. Baker (Sierra Leone); A. Bobogare (Solomon Islands), Jamal Amran, Waqar Butt, Tanya Shew-chuk (Somalia); M.A. Groepe (South Africa); Simon Kunene

(Swaziland); Karimov Sayfuddin (Tajikistan); Nargis Sapa-rova (Tajikistan); Morgah Kodjo (Togo); Seher Topluoglu (Turkey); Kasym Roziyev (Turkmenistan); J.B. Rwakimari (Uganda); Alex Mwita (United Republic of Tanzania); Par-ida Umarova (Uzbekistan); Lenny Warele (Vanuatu); Trinh Dinh Tuong (Viet Nam); Jameel S.Al-Asbahi (Yemen); and LS Charimari (Zimbabwe).

Finally, we thank Sue Hobbs for graphic design and layout, Mary Roll for editing, and Simone Colairo Valerio, Kreena Govender, Joan Griffith and Rosemary Wakeling for admin-istrative support.

WORLD MALARIA REPORT 2008 vii

…Summary

There were an estimated 247 million malaria cases among 3.3 billion people at risk in 2006, causing nearly a mil-lion deaths, mostly of children under 5 years. 109 countries were endemic for malaria in 2008, 45 within the WHO Afri-can region.

The combination of tools and methods to combat malar-ia now includes long-lasting insecticidal nets (LLIN) and artemisinin-based combination therapy (ACT), supported by indoor residual spraying of insecticide (IRS) and inter-mittent preventive treatment in pregnancy (IPT). Despite big increases in the supply of mosquito nets, especially of LLINs in Africa, the number available in 2006 was still far below need in almost all countries. The procurement of antimalarial medicines through public health services also increased sharply, but access to treatment, especially of ACT, was inadequate in all countries surveyed in 2006.

Household surveys and data from national malaria con-trol programmes (NMCPs) show that the coverage of all interventions in 2006 was far lower in most African coun-tries than the 80% target set by the World Health Assem-bly. Supplies of insecticide-treated nets (ITN) to NMCPs were sufficient to protect an estimated 26% of people in 37 African countries. Surveys in 18 African countries found

that 34% of households owned an ITN; 23% of children and 27% of pregnant women slept under an ITN; 38% of children with fever were treated with antimalarial drugs, but only 3% with ACT; and 18% of women used IPT in pregnancy. Only 5 African countries reported IRS cover-age sufficient to protect at least 70% of people at risk of malaria.

In regions other than Africa, intervention coverage is difficult to measure because household surveys are uncom-mon, preventive methods usually target high-risk popula-tions of unknown size, and NMCPs do not report on diagno-sis and treatment in the private sector.

While the link between interventions and their impact is not always clear, at least 7 of 45 African countries/areas with relatively small populations, good surveillance and high intervention coverage reduced malaria cases and deaths by 50% or more between 2000 and 2006 or 2007. In a further 22 countries in other regions of the world, malaria cases fell by 50% or more over the period 2000–2006. However, deeper investigations of impact are needed to confirm that these 29 countries are on course to meet targets for reducing the malaria burden by 2010.

viii WORLD MALARIA REPORT 2008

…Key points

Background and contextA renewed effort to control malaria worldwide, moving towards elimination in some countries, is founded on the latest generation of effective tools and methods for prevention and cure.1. The advent of long-lasting insecticidal nets (LLINs)

and artemisinin-based combination therapy (ACT), plus a revival of support for indoor residual spraying of insecticide (IRS), presents a new opportunity for large-scale malaria control.

2. To accelerate progress in malaria control, the 2005 World Health Assembly (WHA) set targets of 80% coverage for four key interventions: insecticide-treat-ed nets for people at risk; appropriate antimalarial drugs for patients with probable or confirmed malaria; indoor residual spraying of insecticide for households at risk; and intermittent preventive treatment in preg-nancy. The WHA further specified that, as a result of these interventions, malaria cases and deaths per capita should be reduced by 50% between 2000 and 2010, and by 75% between 2005 and 2015.

3. The World malaria report 2008 uses data from routine surveillance ( 100 endemic countries) and household surveys ( 25 countries, mainly in Africa) to meas-ure achievements up to 2006 and, for some aspects of malaria control, to 2007 and 2008. In five main chapters, 30 country profiles and seven annexes, the report describes: (a) the estimated burden of dis-ease in each of the 109 countries and territories with malaria in 2006; (b) how WHO-recommended policies and strategies on malaria control have been adopted, by country, region and globally; (c) the progress made in implementing control measures; (d) the sources of funding for malaria control; and (e) recent evidence that interventions can reduce cases and deaths.

Burden of malaria in 2006, by country, region and globally Half of the world’s population is at risk of malaria, and an estimated 250 million cases led to nearly 1 million deaths in 2006.4. An estimated 3.3 billion people were at risk of malaria

in 2006. Of this total, 2.1 billion were at low risk (< 1reported case per 1000 population), 97% of whom were living in regions other than Africa. The 1.2 bil-

lion at high risk ( 1 case per 1000 population) were living mostly in the WHO African (49%) and South-East Asia regions (37%).

5. There were an estimated 247 million episodes of malar-ia in 2006, with a wide uncertainty interval (5th–95th centiles) from 189 million to 327 million cases. Eighty-six percent, or 212 million (152–287 million) cases, were in the African Region. Eighty percent of the cases in Africa were in 13 countries, and over half were in Nigeria, Democratic Republic of the Congo, Ethiopia, United Republic of Tanzania and Kenya. Among the cases that occurred outside the African Region, 80% were in India, Sudan, Myanmar, Bangladesh, Indone-sia, Papua New Guinea and Pakistan.

6. There were an estimated 881 000 (610 000–1 212 000) malaria deaths in 2006, of which 91% (801 000, range 520 000–1 126 000) were in Africa and 85% were of children under 5 years of age.

7. Estimates of malaria incidence are based, in part, on the numbers of cases reported by national malaria con-trol programmes (NMCPs). These case reports are far from complete in most countries. A total of 94 million malaria cases was reported by national malaria control programmes in 2006, or 37% of the estimated global case incidence. The true proportion of malaria episodes detected by NMCPs would have been lower than 37% because, in some countries, reported cases include patients that are diagnosed clinically but do not have malaria. NMCPs reported 301 000 malaria deaths, or 34% of estimated deaths worldwide in 2006.

Policies and strategies for malaria controlNational malaria control programmes have adopted many of the WHO-recommended policies on prevention and cure, but with variation among countries and regions.8. Nearly all of the 45 countries in the African Region

had adopted, by the end of 2006, the policy of provid-ing insecticidal nets free of charge to children and pregnant women, but only 16 aimed to cover all age groups at risk. ITNs are also used in a high proportion of countries in the South-East Asia and Western Pacific regions, but in relatively few countries in the other three WHO regions.

WORLD MALARIA REPORT 2008 ix

9. Indoor residual spraying is generally used in foci of high malaria transmission. IRS is the dominant method of vector control in the European Region. It is used in fewer countries in Africa, the Americas and South-East Asia, and least in the Western Pacific Region.

10. By June 2008, all except four countries and territo-ries worldwide had adopted ACT as the first-line treat-ment for P. falciparum. Free treatment with ACT was available in 8 of 10 countries in the South-East Asia Region, but a smaller proportion of countries in other regions.

11. The systematic use of intermittent preventive treat-ment in pregnancy is restricted to the African Region; 33 of the 45 African countries had adopted IPT as national policy by the end of 2006.

Preventing malariaDespite big increases in the supply of mosquito nets, especially of long-lasting insecticidal nets in Africa, the number available is still far below need in most countries.12. Between 2004 and 2006, there were modest increases

in the supply of conventional ITNs to countries in the African, South-East Asia and Western Pacific regions, the three regions where nets are most frequently used. By contrast, there was a large increase in the supply of LLINs to countries in the African Region, reaching 36 million in 2006.

13. Based on NMCP records of ITN supplies, however, only six countries in the African Region had sufficient nets (ITNs including LLINs) by 2006 to cover at least 50% of people at risk. These were Ethiopia, Kenya, Mada-gascar, Niger, Sao Tome and Principe, and Zambia. ITN supplies were sufficient to protect 26% of people in 37 African countries that reported in 2006.

14. Among 18 national household surveys carried out in the African Region in 2006–2007, relatively high own-ership and usage of ITNs (including LLINs) was found in Ethiopia, Niger, Sao Tome and Principe, and Zambia. The proportion of family members (children, pregnant women) that slept under an ITN was typically smaller than the proportion of households that owned an ITN. There was wide variation in ITN ownership and use among countries: household ownership of at least 1 net varied from 6% in Côte d’Ivoire to 65% in Niger. Average ITN coverage across the 18 countries with sur-veys was far below the 80% target: 34% of households owned an ITN, and 23% of children under 5 years and 27% of pregnant women slept under an ITN.

15. In regions other than Africa, ITNs are usually targeted at high-risk populations. While the size of these tar-geted populations is not known, NMCP data indicate that relatively high coverage (> 20% of all people at risk) was achieved in Bhutan, Papua New Guinea, Solo-mon Islands and Vanuatu.

16. Indoor residual spraying (IRS) is used focally in all regions of the world. In the African Region, NMCP data indicate that more than 70% of households at any risk of malaria were covered in Botswana, Namibia, Sao Tome and Principe, South Africa and Swaziland. In other regions of the world, relatively high coverage (> 20% of people at risk) was achieved only in Bhutan and Suriname.

Treating malariaThe procurement of antimalarial medicines through public health services increased sharply between 2001 and 2006, but access to treatment, especially of artemisinin-based combination therapy, was inadequate in all countries surveyed in 2006.17. Between 2001 and 2006, NMCPs reported large increases

in the number of courses of antimalarial drugs supplied through public health services. In particular, doses of ACT increased from 6 million in 2005 to 49 million in 2006, of which 45 million were for African countries. These NMCP figures probably underestimate usage, and the exact consumption of ACT is not known.

18. According to NMCP data, only 16 million rapid diagnos-tic tests (RDT) were delivered in 2006, of which 11 mil-lion were for countries in Africa, a small quantity in comparison with the number of malaria episodes.

19. Considering drugs supplied in the public sector (through NMCPs) in relation to estimated malaria cases, as a measure of potential demand, the African countries best-provisioned with any antimalarial drugs in 2006 were Botswana, Comoros, Eritrea, Malawi, Sao Tome and Principe, Senegal, United Republic of Tan-zania and Zimbabwe. Among this group of countries, Eritrea, Sao Tome and Principe, and United Republic of Tanzania were also relatively well supplied with ACT.

20. According to national household surveys, however, none of the populations of 18 African countries sur-veyed in 2006 and 2007 had adequate access to anti-malarial drugs. Only in Benin, Cameroon, Central Afri-can Republic, Gambia, Ghana, Uganda and Zambia were more than 50% of all children with fever treated with an antimalarial drug. In no country did access to treat-ment reach the 80% target, and the average across the 18 countries was 38%. The use of ACT was much lower: just 3% of children on average, ranging from 0.1% in Gambia to 13% in Zambia.

21. A subset of 16 national household surveys found that intermittent preventive treatment (IPT, 2 doses of sulfadoxine-pyrimethamine) was used most frequently by pregnant women in Gambia, Malawi, Senegal and Zambia (33–61%), and by an average of 18% of women in all 16 countries.

22. In regions other than Africa, access to treatment is more difficult to judge: household surveys that include questions on treatment for malaria are much less com-mon and, as in Africa, national control programmes

x WORLD MALARIA REPORT 2008

do not report on diagnosis and treatment in the pri-vate sector. Nevertheless, as far as can be judged from NMCP data, the countries relatively well provisioned with antimalarial drugs were: Bhutan, Lao People’s Democratic Republic, Vanuatu and Viet Nam.

Financing malaria controlFunding for malaria control in 2006 was reported to be greater than ever before, but it is not yet possible to judge from NMCP budgets which countries have adequate resources for malaria control.23. According to NMCP data for 2006, the African Region

had more funds for malaria control than any other, and reported a larger increase in funding than any other region between 2004 and 2006. However, the total of US$ 688 million for the African Region in 2006 is cer-tain to be an underestimate because reports were sub-mitted by only 26 of 45 countries. The US$ 4.6 avail-able per (estimated) malaria case in the 26 reporting countries is unlikely to be adequate to meet targets for prevention and cure.

24. The major sources of extra funds for African coun-tries between 2004 and 2006 were reported to be the national governments of the affected countries plus the Global Fund to Fight AIDS, Tuberculosis and Malar-ia. These two sources dominated funding for malaria control in the African Region and worldwide in 2006.

25. The balance of funding support varied among WHO regions. In the Americas, the European and South-East Asia regions, the majority of funds were from the gov-ernments of endemic countries. In the Eastern Medi-terranean and Western Pacific regions, the Global Fund was reported to be the principal source of financial support. The Western Pacific Region placed greatest reliance on external funding, followed by the African and Eastern Mediterranean regions. Countries in the African Region presented the most diverse portfolio of support from external agencies.

Impact of malaria controlSome countries that have implemented aggressive programmes of prevention and treatment, in Africa and other regions, have reported significant reductions in the malaria burden.26. While the effect of malaria control can be evaluated by

repeated population surveys – of parasite prevalence, anemia, malaria-specific mortality or all-cause mortal-ity – this report focuses on the inferences that can be drawn from national surveillance reports.

27. Among 41 African countries that provided case and death reports over the period 1997–2006, the most per-suasive evidence for impact comes from four countries,

or parts of countries, with relatively small populations, good surveillance, and high intervention coverage. They are Eritrea, Rwanda, Sao Tome and Principe, and Zanzibar (United Republic of Tanzania). All four coun-tries/areas reduced the malaria burden by 50% or more between 2000 and 2006–2007, in line with WHA tar-gets.

28. In other African countries where a high proportion of people have access to antimalarial drugs or insecticid-al nets, such as Ethiopia, Gambia, Kenya, Mali, Niger and Togo, routine surveillance data do not yet show, unequivocally, the expected reductions in morbidity and mortality. Either the data are incomplete, or the effects of interventions are small.

29. The reportedly high coverage of indoor residual spray-ing in Namibia, South Africa and Swaziland is consist-ent with the observed declines in case numbers in these countries, and evidently builds on earlier suc-cesses achieved with IRS.

30. Surveillance reports for many countries outside Afri-ca indicate that malaria declined during the decade 1997–2006. Malaria cases were falling in at least 25 endemic countries in five WHO regions. In 22 of these countries, the number of reported cases fell by 50% or more between 2000 and 2006–2007, in line with WHA targets.

31. The recorded number of malaria deaths has fallen in at least six countries in the Americas, and in the South-East Asia and Western Pacific regions. These countries are Cambodia, Lao People’s Democratic Republic, Phil-ippines, Suriname, Thailand and Viet Nam, and all six are on course to meet WHA targets for reductions in malaria mortality by 2010.

32. Reductions in cases and deaths can be linked to spe-cific interventions in some countries, for example the targeted use of ITNs in Cambodia, India, Lao People’s Democratic Republic and Viet Nam. In general, howev-er, the links between interventions and trends remain ambiguous, and more careful investigations of the effects of control are needed in most countries.

33. WHO has identified four phases on the path to malaria elimination. By July 2008, the 109 countries/territories affected by malaria were classified as follows: control (82), pre-elimination (11), elimination (10), and the prevention of reintroduction (6). In January 2007, the United Arab Emirates was the first formerly-endemic country since the 1980s to be certified malaria-free by WHO, bringing the total number of malaria-free coun-tries/territories to 92.

WORLD MALARIA REPORT 2008 xi

…Résumé

Sur 3,3 milliards de personnes à risque en 2006, on estime à 247 millions le nombre de cas de paludisme, dont près d’un million de cas mortels, pour la plupart chez les enfants de moins de cinq ans. En 2008, le paludisme était endémique dans 109 pays, dont 45 sont situés dans la Région africaine de l’OMS.

L’arsenal de produits et de méthodes pour combattre le paludisme comprend maintenant les moustiquaires à imprégnation durable et les associations médicamenteuses à base d’artémisinine (ACT), conjuguées à la pulvérisation intradomiciliaire à effet rémanent et au traitement pré-ventif intermittent pendant la grossesse. Malgré la forte augmentation de l’offre de moustiquaires, notamment de moustiquaires à imprégnation durable en Afrique, les quan-tités disponibles en 2006 étaient encore bien inférieures aux besoins dans presque tous les pays. L’approvisionne-ment en médicaments antipaludiques par l’intermédiaire des services de santé publique s’est lui aussi fortement accru, mais l’accès au traitement, en particulier aux ACT, était insuffisant dans tous les pays enquêtés en 2006.

Les enquêtes effectuées auprès des ménages et les don-nées des programmes nationaux de lutte contre le paludis-me (PNLPs) montrent qu’en 2006, la couverture de toutes les interventions dans la plupart des pays africains était bien inférieure à la cible de 80 % fixée par l’Assemblée mondiale de la santé. L’offre des moustiquaires imprégnées d’insecticide aux PNLPs était suffisante pour protéger une estimation de 26% des personnes dans 37 pays afri-cains. Les enquêtes dans 18 pays africains ont trouvé que

34 % des ménages possédaient une moustiquaire impré-gnée d’insecticide ; 23 % des enfants et 27 % des femmes enceintes dormaient sous des moustiquaires de ce type ; 38 % des enfants ayant de la fièvre se voyaient administrer des médicaments antipaludiques, mais 3 % seulement des ACT ; et 18 % des femmes suivaient un traitement préven-tif intermittent pendant la grossesse. Seulement cinq pays africains faisaient état d’une couverture de la pulvérisation intradomiciliaire à effet rémanent suffisante pour protéger au moins 70 % des personnes exposées au paludisme.

Dans les régions autres que l’Afrique, la couverture des interventions est difficile à mesurer parce que les Les enquêtes effectuées auprès des ménages sont peu fré-quentes, les méthodes préventives ciblent généralement les populations à haut risque de taille inconnue, et les PNLPs ne présentent pas de rapport sur le diagnostic et le traitement dans le secteur privé.

Bien que le lien entre les interventions et leurs effets n’apparaisse pas toujours clairement, au moins sept des 45 pays et zones d’Afrique où la population est relative-ment peu nombreuse, la surveillance bonne et la couver-ture des interventions élevée sont parvenus à réduire la morbidité et la mortalité palustres d’au moins 50 % entre 2000 et 2006 ou 2007. Dans au moins 22 pays situés dans d’autres régions du monde, le nombre de cas de paludisme a chuté de 50 % entre 2000 et 2006. D’après les données obtenues par surveillance systématique, au moins 29 des 109 pays concernés dans le monde sont en voie d’atteindre les cibles fixées pour 2010.

xii WORLD MALARIA REPORT 2008

…Points essentiels

Aperçu général et contexteUn nouvel effort est entrepris contre le paludisme dans le monde, tendant à l’élimination de la maladie dans certains pays, à l’aide de la dernière génération de produits et de méthodes préventifs et curatifs.1. L’avènement des moustiquaires à imprégnation durable

et des associations médicamenteuses à base d’artémi-sinine (ACT), ainsi que la remise à l’honneur de la pul-vérisation intradomiciliaire à effet rémanent offrent une nouvelle possibilité de lutter contre le paludisme à grande échelle.

2. En 2005, pour progresser plus rapidement dans le com-bat contre le paludisme, l’Assemblée mondiale de la Santé a fixé pour cible une couverture d’au moins 80 %pour quatre interventions : moustiquaires imprégnées d’insecticide pour les personnes à risque ; médica-ments antipaludiques appropriés pour les cas de palu-disme probables ou confirmés ; pulvérisation intrado-miciliaire à effet rémanent pour les ménages à risque ;et traitement préventif intermittent pendant la gros-sesse. L’Assemblée de la Santé a en outre précisé que, grâce à ces interventions, la morbidité et la mortalité palustres par habitant devraient diminuer d’au moins 50 % entre 2000 et 2010 et d’au moins 75 % entre 2005 et 2015.

3. Le Rapport sur le paludisme dans le monde, 2008 se fonde sur des données issues de la surveillance systématique (dans une centaine de pays d’endémie) et d’enquêtes auprès des ménages (dans quelque 25 pays, principa-lement en Afrique) pour mesurer les progrès accomplis jusqu’en 2006 et, concernant certains aspects de la lutte antipaludique, jusqu’en 2007 et 2008. Composé de cinq grands chapitres, 30 profils de pays et sept annexes, le rapport indique : a) la charge estimative de la maladie dans chacun des 109 pays et territoires impaludés en 2006 ; b) comment les politiques et stra-tégies de lutte antipaludique recommandées par l’OMS ont été adoptées, par pays, par Région et dans l’ensem-ble du monde ; c) les progrès réalisés dans l’application des mesures de lutte ; d) les sources de financement de la lutte antipaludique ; et e) les éléments récents attestant que les interventions permettent de réduire la morbidité et la mortalité.

Charge du paludisme en 2006, par pays, par Région et au niveau mondialLa moitié de la population mondiale est exposée au risque de paludisme et on estime à 250 millions le nombre de cas en 2006, dont près d’un million de cas mortels.4. On estime que 3,3 milliards de personnes étaient expo-

sées au risque de paludisme en 2006. Sur ce total, 2,1 milliards couraient un risque faible (moins d’un cas signalé pour 1000 habitants) et vivaient pour 97 %ailleurs qu’en Afrique. Au nombre de 1,2 milliard, les personnes exposées à un risque élevé (au moins un cas pour 1000 habitants) vivaient en majorité dans les Régions africaine (49 %) et de l’Asie du Sud-Est (37 %) de l’OMS.

5. On estime à 247 millions le nombre d’épisodes de palu-disme en 2006, avec un intervalle d’incertitude impor-tant (5e–95e percentiles), compris entre 189 millions et 327 millions. Quatre-vingt-six pour cent des cas, ou 212 millions (152–287 millions), se sont produits dans la Région africaine. Quatre-vingts pour cent des cas recensés en Afrique étaient concentrés dans 13 pays, et plus de la moitié au Nigéria, en République démo-cratique du Congo, en Ethiopie, en République-Unie de Tanzanie et au Kenya. Quatre-vingts pour cent des cas observés en dehors de la Région africaine se sont produits en Inde, au Soudan, au Myanmar, au Bangla-desh, en Indonésie, en Papouasie-Nouvelle-Guinée et au Pakistan.

6. On estime à 881 000 (610 000–1 212 000) le nombre de décès par paludisme en 2006, dont 91 % (801 000, fourchette 520 000–1 126 000) ont eu lieu en Afrique et 85 % chez les enfants de moins de cinq ans.

7. Les estimations de l’incidence du paludisme s’appuient en partie sur le nombre de cas déclarés par les pro-grammes nationaux de lutte antipaludique. Dans la plupart des pays, ce chiffre est loin d’être complet. Au total, les programmes nationaux de lutte antipalu-dique ont déclaré 94 millions de cas de paludisme en 2006, ce qui représente 37 % de l’incidence estimative à l’échelle mondiale. La proportion réelle d’épisodes de paludisme enregistrés par les programmes nationaux aurait été inférieure à 37 % car, dans certains pays, les cas déclarés englobent les patients chez qui le paludis-me est diagnostiqué cliniquement alors qu’ils ne sont

WORLD MALARIA REPORT 2008 xiii

pas atteints de la maladie. Les programmes nationaux ont déclaré 301 000 décès par paludisme, soit 34 % de la mortalité estimative à l’échelle mondiale en 2006.

Politiques et stratégies de lutte antipaludiqueLes programmes nationaux de lutte antipaludique ont adopté bon nombre de stratégies préventives et curatives recommandées par l’OMS, mais il y a des écarts entre pays et Régions.8. Fin 2006, les 45 pays de la Région africaine avaient

presque tous adopté pour politique de fournir gratui-tement des moustiquaires imprégnées d’insecticide aux femmes enceintes et aux enfants, mais seulement 16 d’entre eux entendaient couvrir toutes les tranches d’âge à risque. Les moustiquaires imprégnées d’insec-ticide sont également utilisées dans une grande pro-portion des pays des Régions de l’Asie du Sud-Est et du Pacifique occidental, mais dans relativement peu de pays dans les trois autres Régions de l’OMS.

9. La pulvérisation intradomiciliaire à effet rémanent est une méthode généralement utilisée dans les foyers de forte transmission du paludisme. C’est la principale méthode de lutte antivectorielle dans la Région euro-péenne. Les pays sont moins nombreux à l’utiliser en Afrique, dans les Amériques et en Asie du Sud-Est, et c’est dans la Région du Pacifique occidental qu’elle est le moins utilisée.

10. En juin 2008, tous les pays et territoires, à l’exception de quatre d’entre eux, avaient opté pour les ACT com-me traitement de choix contre P. falciparum. Le traite-ment par ACT était disponible gratuitement dans huit pays sur 10 dans la Région de l’Asie du Sud-Est, mais la proportion était moindre dans les autres Régions.

11. Le recours systématique au traitement préventif inter-mittent pendant la grossesse se limite à la Région africaine ; fin 2006, 33 des 45 pays africains l’avaient adopté comme politique nationale.

Prévention du paludismeMalgré la forte augmentation de l’offre de moustiquaires, notamment de moustiquaires à imprégnation durable en Afrique, les quantités disponibles étaient encore bien inférieures aux besoins dans presque tous les pays.12. Entre 2004 et 2006, l’offre de moustiquaires impré-

gnées d’insecticide a augmenté modérément dans les pays de la Région africaine et des Régions de l’Asie du Sud-Est et du Pacifique occidental, qui sont celles où les moustiquaires sont le plus utilisées. En revanche, l’offre de moustiquaires à imprégnation durable dans les pays de la Région africaine a beaucoup augmenté et atteignait 36 millions en 2006.

13. Cependant, d’après les quantités de moustiquaires imprégnées d’insecticide fournies par les programmes nationaux de lutte antipaludique, seuls six pays de la Région africaine avaient suffisamment de mous-tiquaires de ce type (y compris les moustiquaires à

imprégnation durable) en 2006 pour protéger au moins 50 % des personnes à risque. Il s’agissait de l’Ethiopie, du Kenya, de Madagascar, du Niger, de Sao Tomé-et-Principe et de la Zambie.

14. D’après les 18 enquêtes nationales réalisées auprès des ménages dans la Région africaine en 2006–2007, la population était relativement nombreuse à possé-der et à utiliser des moustiquaires imprégnées d’in-secticide (y compris des moustiquaires à imprégnation durable) en Ethiopie, au Niger, à Sao Tomé-et-Principe et en Zambie. La proportion de membres de la famille (enfants, femmes enceintes) dormant sous une mous-tiquaire imprégnée d’insecticide était généralement inférieure à la proportion de ménages possédant une moustiquaire de ce type. La possession et l’utilisation des moustiquaires imprégnées d’insecticide étaient très variables selon les pays : en Côte d’Ivoire, 6 % seu-lement des ménages possédaient au moins une mous-tiquaire, contre 65 % au Niger. La couverture moyenne des moustiquaires imprégnées d’insecticide dans les 18 pays où des enquêtes ont été effectuées était bien inférieure à la cible de 80 % : 34 % des ménages en possédaient une, et 23 % des enfants de moins de cinq ans et 27 % des femmes enceintes dormaient sous une moustiquaire de ce type.

15. Ailleurs qu’en Afrique, les moustiquaires imprégnées d’insecticide sont généralement destinées aux popu-lations à haut risque. On ignore la taille des popula-tions visées, mais d’après les données des programmes nationaux de lutte antipaludique, la couverture est relativement élevée (supérieure à 20 % de toutes les personnes à risque) au Bhoutan, en Papouasie-Nouvel-le-Guinée, dans les Iles Salomon et au Vanuatu.

16. La pulvérisation intradomiciliaire à effet rémanent est une méthode utilisée dans les foyers de transmis-sion dans toutes les régions du monde. Dans la Région africaine, les données des programmes nationaux de lutte antipaludique indiquent que plus de 70 % des ménages exposés à un risque quelconque de paludisme bénéficiaient de cette intervention au Botswana, en Namibie, à Sao Tomé-et-Principe, en Afrique du Sud et au Swaziland. Dans les autres régions du monde, seuls le Bhoutan et le Suriname enregistrent une couverture relativement élevée (supérieure à 20 % des personnes à risque).

Traitement du paludismeL’approvisionnement en médicaments antipaludiques par l’intermédiaire des services de santé publique s’est fortement accru entre 2001 et 2006, mais l’accès au traitement, en particulier aux associations médicamenteuses à base d’artémisinine, était insuffisant dans tous les pays enquêtés en 2006.17. Entre 2001 et 2006, les programmes nationaux de lutte

antipaludique ont rapporté une forte augmentation du nombre de traitements d’antipaludiques fournis par les

xiv WORLD MALARIA REPORT 2008

services de santé publique. Le nombre de doses d’ACT est notamment passé de 6 millions en 2005 à 49 mil-lions en 2006, sur lesquels 45 millions de doses étaient destinées aux pays africains. Ces chiffres sont proba-blement des sous-estimations et on ignore la consom-mation exacte d’ACT.

18. D’après les données des programmes nationaux, seu-lement 16 millions de tests diagnostiques rapides ont été fournis en 2006, dont 11 millions pour des pays d’Afrique, ce qui est peu par rapport au nombre d’épi-sodes palustres.

19. Si l’on compare la quantité de médicaments fournis dans le secteur public (dans le cadre des programmes nationaux de lutte antipaludique) au nombre estimatif de cas en tant que mesure de la demande potentielle, les pays africains les mieux pourvus en antipaludiques en 2006 étaient le Botswana, les Comores, l’Erythrée, le Malawi, Sao Tomé-et-Principe, le Sénégal, la Répu-blique-Unie de Tanzanie et le Zimbabwe. Dans ce groupe de pays, l’Erythrée, Sao Tomé-et-Principe et la République-Unie de Tanzanie étaient aussi relative-ment bien approvisionnés en ACT.

20. Cependant, d’après les enquêtes nationales auprès des ménages, les médicaments antipaludiques n’étaient suffisamment accessibles à la population dans aucun des 18 pays africains enquêtés en 2006 et 2007. Il n’y a qu’au Bénin, au Cameroun, en République centrafri-caine, en Gambie, au Ghana, en Ouganda et en Zambie que plus de 50 % des enfants ayant de la fièvre étaient mis sous traitement antipaludique. Dans aucun pays l’accès au traitement atteignait la cible de 80 %, et la moyenne dans l’ensemble des 18 pays était de 38 %. L’utilisation des ACT était bien plus faible : seulement 3 % des enfants en moyenne, allant de 0,1 % en Gam-bie à 13 % en Zambie.

21. D’après un sous-ensemble de 16 enquêtes nationales auprès des ménages, c’est en Gambie, au Malawi, au Sénégal et en Zambie que l’usage du traitement pré-ventif intermittent (au moins deux doses de sulfado-xine-pyriméthamine) était le plus répandu chez les femmes enceintes (33 %–61 %) et 18 % des femmes en moyenne l’utilisaient dans les 16 pays concernés.

22. Ailleurs qu’en Afrique, il est plus difficile d’apprécier l’accès au traitement : les enquêtes comprenant des questions sur le traitement du paludisme sont bien moins courantes et, comme en Afrique, les program-mes nationaux de lutte antipaludique ne renseignent pas sur le diagnostic et le traitement dans le secteur privé. Néanmoins, d’après ce qu’indiquent les données des programmes nationaux, les pays relativement bien pourvus en médicaments antipaludiques sont le Bhoutan, la République démocratique populaire lao, le Vanuatu et le Viet Nam.

Financement de la lutte antipaludiqueLe financement de la lutte antipaludique a atteint un niveau sans précédent en 2006, mais on ne peut pas encore dire d’après le budget des programmes nationaux de lutte antipaludique quels pays ont suffisamment de ressources pour combattre la maladie.23. D’après les données des programmes nationaux pour

2006, la Région africaine était celle qui avait le plus de fonds pour la lutte antipaludique et qui faisait état de la plus forte augmentation du financement entre 2004 et 2006. Il est toutefois certain que le budget total de US $688 millions pour la Région africaine en 2006 est une sous-estimation car seuls 26 des 45 pays africains ont communiqué des informations. La somme de US $4,6 par cas (estimatif) de paludisme dans les 26 pays déclarants ne devrait pas suffire pour atteindre les cibles de prévention et de guérison.

24. Selon les informations obtenues, les principales sour-ces de fonds supplémentaires pour les pays africains entre 2004 et 2006 étaient les gouvernements des pays impaludés et le Fonds mondial de lutte contre le sida, la tuberculose et le paludisme. Ces deux sources de financement de la lutte antipaludique étaient les plus importantes dans la Région africaine et dans le monde en 2006.

25. L’importance respective des sources de financement variait entre les Régions de l’OMS. Dans la Région euro-péenne et dans les Régions des Amériques et de l’Asie du Sud-Est, les fonds provenaient en majorité des gou-vernements des pays d’endémie. Dans les Régions de la Méditerranée orientale et du Pacifique occidental, la principale source d’appui financier était le Fonds mon-dial. La Région du Pacifique occidental était celle qui dépendait le plus du financement extérieur, suivie des Régions africaine et de la Méditerranée orientale. Les pays de la Région africaine étaient ceux qui présen-taient la plus grande diversité de bailleurs de fonds extérieurs.

Efficacité de la lutte antipaludiqueCertains pays d’Afrique et d’autres Régions qui ont appliqué des programmes énergiques dans les domaines préventif et curatif ont fait état d’une diminution importante de la charge du paludisme.26. Les effets de la lutte antipaludique peuvent être éva-

lués par des séries d’enquêtes en population sur la pré-valence du parasite, l’anémie, la mortalité palustre ou la mortalité toutes causes confondues, mais le présent rapport s’intéresse aux conclusions que l’on peut tirer des rapports de surveillance nationaux.

27. Parmi les 41 pays africains qui ont fourni des chif-fres sur la morbidité et la mortalité pendant la période 1997–2006, quatre pays ou zones à l’intérieur des pays, caractérisés par une population relativement peu nombreuse, une bonne surveillance et une couverture élevée des interventions, enregistrent les résultats les

WORLD MALARIA REPORT 2008 xv

plus probants. Il s’agit de l’Erythrée, du Rwanda, de Sao Tomé-et-Principe et de Zanzibar (République-Unie de Tanzanie). Dans ces quatre pays ou zones, la charge du paludisme a diminué d’au moins 50 % entre 2000 et 2006–2007, conformément aux cibles fixées par l’As-semblée de la Santé.

28. Dans les autres pays africains où une forte proportion d’habitants ont accès aux médicaments antipaludiques ou aux moustiquaires imprégnées d’insecticide comme l’Ethiopie, la Gambie, le Kenya, le Mali, le Niger et le Togo, les données issues de la surveillance systématique ne reflètent pas encore clairement le recul attendu de la morbidité et de la mortalité. Soit les données sont incomplètes, soit les interventions ont peu d’effet.

29. La couverture élevée de la pulvérisation intradomici-liaire à effet rémanent dont il est fait état en Namibie, en Afrique du Sud et au Swaziland concorde avec la baisse du nombre de cas constatée dans ces pays et va dans le sens des succès déjà enregistrés grâce à cette méthode.

30. D’après les rapports de surveillance de nombreux pays situés ailleurs qu’en Afrique, le paludisme a reculé pen-dant la décennie 1997-2006. Le nombre de cas dimi-nuait dans au moins 25 pays d’endémie, situés dans cinq Régions de l’OMS. Dans 22 de ces pays, le nombre de cas déclarés a chuté de 50 % voire plus entre 2000 et 2006–2007, conformément aux cibles fixées par l’As-semblée de la Santé.

31. Le nombre déclaré de cas de paludisme a baissé dans au moins six pays des Régions des Amériques, de l’Asie du Sud-Est et du Pacifique occidental. Il s’agit du Cam-bodge, de la République démocratique populaire lao, des Philippines, du Suriname, de la Thaïlande et du Viet Nam, et tous sont en voie d’atteindre les cibles de l’Assemblée de la Santé concernant la réduction de la mortalité palustre d’ici à 2010.

32. La baisse de la morbidité et de la mortalité peut être associée à des interventions spécifiques dans certains pays, par exemple à l’utilisation ciblée des moustiquai-res imprégnées d’insecticide au Cambodge, en Inde, en République démocratique populaire lao et au Viet Nam. D’une manière générale, cependant, le lien entre les interventions et les tendances restent ambigu et il faut étudier plus attentivement les effets de la lutte antipaludique dans la plupart des pays.

33. L’OMS a défini quatre phases dans le processus d’éli-mination du paludisme. En juillet 2008, les 109 pays et territoires impaludés se répartissaient comme suit :lutte (82), préélimination (11), élimination (10) et prévention de la réintroduction (6). En janvier 2007, les Emirats arabes unis ont été le premier ancien pays d’endémie à être certifié exempt de paludisme par l’OMS depuis les années 80, ce qui porte à 92 le nombre total de pays et territoires exempts de paludisme.

xvi WORLD MALARIA REPORT 2008

…Resumen

En 2006 se registraron según las estimaciones unos 247 millones de casos de malaria entre 3300 millones de per-sonas en riesgo, produciéndose como resultado casi un millón de muertes, principalmente de menores de cinco años. En 2008 había 109 países con malaria endémica, 45 de ellos en la Región de África de la OMS.

Entre los medios disponibles para combatir la malaria destacan los mosquiteros tratados con insecticidas de lar-ga duración (MILD) y el tratamiento combinado basado en la artemisinina (TCA), que están respaldados por el rocia-miento de interiores con insecticidas de acción residual (RIR) y el tratamiento preventivo intermitente durante el embarazo (TPI). Pese al gran aumento del suministro de mosquiteros, especialmente de MILD en África, el número disponible en 2006 se mantuvo todavía muy por debajo de las necesidades en casi todos los países. La adquisición de medicamentos antimaláricos a través de los servicios de salud pública también aumentó de forma pronunciada, pero el acceso al tratamiento, especialmente al TAC, fue insuficiente en todos los países encuestados en 2006.

Las encuestas de hogares y los datos de los programas nacionales de control de la malaria (PNCM) revelaron que la cobertura de todas las intervenciones en 2006 fue muy inferior a la meta del 80% fijada por la Asamblea Mundial de la Salud en la mayoría de los países africanos. Se estima que el suministro de mosquitero tratado con insecticida (MTI) a los PNCM era suficiente para proteger aproxima-damente el 26% de la población en 37 países africanos.

Encuestas realizadas en 18 países africanos revelan que el 34% de los hogares poseían un MTI ; el 23% de los niños y el 27% de las mujeres embarazadas dormían bajo un MTI; el 38% de los niños con fiebre fueron tratados con antimaláricos, pero sólo un 3% con TAC; y el 18% de las mujeres recibieron TPI durante el embarazo. Sólo 5 países africanos notificaron una cobertura de hogares suficiente para proteger al menos al 70% de las personas con riesgo de malaria.

En las otras regiones del mundo, diferentes al África, la cobertura de las intervenciones es difícil de medir debido a que las encuestas de hogares no son frecuentes, los méto-dos preventivos generalmente se focalizan sobre grupos de alto riesgo de tamaño desconocido, y los PNCM no reportan datos sobre diagnóstico y tratamiento en el sector privado.

Aunque la conexión entre las intervenciones y su impac-to no siempre está clara, al menos 7 de 45 países o zonas africanas con poblaciones relativamente pequeñas, una buena vigilancia y una cobertura de intervención alta lograron reducir los casos de malaria y la mortalidad por esa causa en un 50% o más entre 2000 y 2006–2007. Asi-mismo, en al menos 22 de 64 países de otras regiones del mundo, los casos de malaria disminuyeron un 50% durante el periodo 2000–2006. No obstante, investigaciones más profundas acerca del impacto se necesitarán para confirmar que estos 29 países están bien encaminados para alcanzar las metas de reducción de la carga de malaria fijadas para 2010.

WORLD MALARIA REPORT 2008 xvii

…Puntos clave

Antecedentes y contextoLa renovación de los esfuerzos para controlar la malaria en todo el mundo, y aproximarse a la eliminación en algunos países, se basa en la última generación de medios eficaces de prevención y curación. 1. La irrupción de los mosquiteros tratados con insecti-

cidas de larga duración (MILD) y el tratamiento com-binado basado en la artemisinina (TAC), unidos a la reactivación del apoyo al rociamiento de interiores con insecticidas de acción residual (RIR), brinda una nueva oportunidad para controlar la malaria en gran escala.

2. A fin de acelerar los progresos de la lucha contra la malaria, la Asamblea Mundial de la Salud (AMS) de 2005 estableció metas de cobertura 80% para cua-tro intervenciones clave: mosquiteros tratados con insecticida para las personas en riesgo; medicamentos antimaláricos apropiados para los enfermos con mala-ria probable o confirmada; rociamiento de interiores con insecticidas de acción residual para los hogares en riesgo; y tratamiento preventivo intermitente duran-te el embarazo. La AMS especificó además que, como resultado de esas intervenciones, los casos de malaria y la mortalidad por esa causa deberían reducirse al menos en un 50% entre 2000 y 2010, y al menos en un 75% entre 2005 y 2015.

3. El Informe mundial sobre el paludismo 2008 usa datos procedentes de la vigilancia rutinaria ( 100 países endémicos) y de encuestas de hogares ( 25 países, principalmente africanos) para medir los logros regis-trados hasta 2006 y, para algunos aspectos del control de la malaria, hasta 2007 y 2008. En cinco capítulos principales, 30 perfiles de países y siete anexos, el informe describe: (a) la carga de morbilidad estimada en cada uno de los 109 países y territorios con malaria en 2006; (b) cómo se han adoptado, en los países, las regiones y a nivel mundial, las políticas y estrategias recomendadas por la OMS para combatir la malaria; (c) los progresos realizados en la aplicación de las medi-das de control; (d) las fuentes de financiación del con-trol de la malaria; y (e) datos probatorios recientes de que las intervenciones pueden reducir el número de casos y de defunciones.

Carga de malaria en 2006, por países y regiones y a nivel mundialLa mitad de la población mundial está expuesta al riesgo de contraer malaria, y unos 250 millones de casos de la enfermedad provocaron casi un millón de muertes en 2006. 4. En 2006 había unos 3300 millones de personas en ries-

go de sufrir malaria. De esa cifra, 2100 millones esta-ban expuestas a un riesgo bajo (< 1 caso declarado por 1000 habitantes), el 97% fuera de África. Los 1200 millones con riesgo alto ( 1 caso por 1000 habitan-tes) vivían principalmente en las regiones de África (49%) y Asia Sudoriental (37%) de la OMS.

5. Se estima que en 2006 se registraron unos 247 millones de episodios de malaria, con un amplio intervalo de incertidumbre (percentiles 5 a 95): 18–327 millones. Un 86% de los casos, 212 millones (152–287 millones), se dieron en la Región de África. Un 80% de los casos registrados en este continente se concentraron en 13 países, y más de la mitad correspondieron a Nigeria, la República Democrática del Congo, Etiopía, la República Unida de Tanzanía y Kenya. Entre los casos registra-dos fuera de la Región de África, el 80% se dieron en la India, el Sudán, Myanmar, Bangladesh, Indonesia, Papua Nueva Guinea y el Pakistán.

6. Se estima que en 2006 hubo 881 000 (610 000–1 212 000) defunciones por malaria, el 91% de las cuales (801 000, intervalo: 520 000–1 126 000) se registraron en África, y el 85% entre menores de cinco años.

7. Las estimaciones sobre la incidencia de malaria se basan en parte en el número de casos notificados por los programas nacionales de control de la malaria (PNCM). Esos informes de casos distan mucho de estar completos en la mayoría de los países. Los programas nacionales de control de la malaria notificaron en total en 2006 94 millones de casos, o el 37% de la incidencia mundial de casos estimada. La proporción real de epi-sodios de malaria detectados por los PNCM habría sido inferior a ese 37% debido a que, en algunos países, entre los casos notificados figuran pacientes con diag-nóstico clínico de malaria que sin embargo no sufren la enfermedad. Los PNCM notificaron 301 000 muertes por malaria, lo que supone un 34% de las defunciones estimadas en todo el mundo en 2006.

xviii WORLD MALARIA REPORT 2008

Políticas y estrategias de control de la malariaLos programas nacionales de control de la malaria han adoptado muchas de las políticas recomendadas por la OMS en materia de prevención y curación, pero con diferencias entre países y regiones. 8. La casi totalidad de los 45 países de la Región de África

habían adoptado al final de 2006 la política de propor-cionar mosquiteros tratados con insecticida gratuita-mente a los niños y las mujeres embarazadas, pero sólo 16 de ellos se proponían abarcar a todos los grupos de edad en riesgo. Los MTI se usan también en un alto por-centaje de países en las Regiones de Asia Sudoriental y el Pacífico Occidental, pero en relativamente pocos países en las otras tres regiones de la OMS.

9. El rociamiento de interiores con insecticidas de acción residual se practica en general en los focos de alta trans-misión de la malaria. El RIR es el método predominante de lucha antivectorial en la Región de Europa. Se usa en menos países en África, las Américas y Asia Sudoriental, y aún menos en la Región del Pacífico Occidental.

10. En junio de 2008, todos excepto cuatro países y terri-torios mundiales habían adoptado el TCA como trata-miento de primera línea contra P. falciparum. Se podía conseguir tratamiento gratuito con TCA en 8 de 10 países de la Región de Asia Sudoriental, pero en una proporción más pequeña de países en otras regiones.

11. La aplicación sistemática del tratamiento preventivo intermitente durante el embarazo está limitada a la Región de África; 33 de los 45 países africanos habían adoptado el TPI como política nacional al final de 2006.

Prevención de la malariaA pesar del gran aumento del suministro de mosquiteros, especialmente de mosquiteros con insecticidas de larga duración en África, el número disponible está todavía muy por debajo de las necesidades en la mayoría de los países. 12. Entre 2004 y 2006 aumentó de forma moderada el sumi-

nistro de MTI tradicionales a los países de las regiones de África, Asia Sudoriental y el Pacífico Occidental, las tres regiones donde más frecuente es el uso de mos-quiteros. Muy importante fue en cambio el incremento del suministro de MILD a los países de la Región de África, alcanzándose la cifra de 36 millones en 2006.

13. A juzgar por los registros de los suministros de MTI de los PNCM, sin embargo, sólo seis países de la Región de África disponían en 2006 de mosquiteros suficientes (MTI, incluidos MILD) para garantizar la cobertura de al menos un 50% de las personas en riesgo: Etiopía, Kenya, Madagascar, Níger, Santo Tomé y Príncipe, y Zambia.

14. Entre las 18 encuestas nacionales de hogares llevadas a cabo en la Región de África en 2006–2007, se obser-vó una tenencia y uso relativamente elevados de MTI (incluidos MILD) en Etiopía, el Níger, Santo Tomé y

Príncipe y Zambia. La proporción de familiares (niños, mujeres embarazadas) que dormían bajo un MTI fue por lo general menor que la proporción de hogares que poseían un MTI. Se observaron grandes diferencias en lo que atañe a la tenencia y uso de MTI entre los paí-ses: el número de hogares con al menos un mosquite-ro oscilaba entre el 6% de Côte d’Ivoire y el 65% del Níger. La cobertura media con MTI entre los 18 países con encuestas fue muy inferior a la meta del 80%: el 34% de los hogares poseían un MTI, y sólo el 23% de los menores de cinco años y el 27% de las mujeres embarazadas dormían protegidos por un MTI.

15. Fuera de África, en las otras regiones, los MTI están dirigidos generalmente a las poblaciones de alto ries-go. Aunque se desconoce el tamaño de esas poblacio-nes destinatarias, los datos de los PNCM indican que en Bhután, Papua Nueva Guinea, las Islas Salomón y Vanuatu se alcanzó una cobertura relativamente alta ( > 20% de todas las personas en riesgo).

16. El rociamiento de interiores con insecticidas de acción residual (RIR) se practica de manera focalizada en todas las regiones del mundo. En la Región de África, los datos de los PNCM indican que más del 70% de los hogares con algún riesgo de malaria estaban cubiertos en Botswana, Namibia, Santo Tomé y Príncipe, Sudáfri-ca y Swazilandia. En las otras regiones del mundo, sólo en Bhután y Suriname se logró una cobertura relativa-mente alta ( > 20% de las personas en riesgo).

Tratamiento de la malariaLa adquisición de medicamentos antimaláricos a través de los servicios de salud pública aumentó pronunciadamente entre 2001 y 2006, pero el acceso al tratamiento, especialmente al tratamiento combinado basado en la artemisinina, fue insuficiente en todos los países encuestados en 2006. 17. Entre 2001 y 2006, los PNCM informaron de grandes

aumentos del número de regímenes de medicamentos antimaláricos suministrados a través de los servicios de salud pública. En particular, las dosis de TCA aumenta-ron de 6 millones en 2005 a 49 millones en 2006, 45 millones de las cuales fueron para países africanos. Probablemente esas cifras de los PNCM subestiman el uso real, de modo que se desconoce el consumo exacto de TCA.

18. Según los datos de los PNCM, en 2006 sólo se suminis-traron 16 millones de pruebas de diagnóstico rápido (PDR), 11 millones de las cuales fueron para países africanos, lo que representa una cantidad pequeña en comparación con el número de episodios de malaria.

19. Considerando la relación entre los medicamentos sumi-nistrados en el sector público (a través de los PNCM) y los casos de malaria estimados, como reflejo de la demanda potencial, los países africanos mejor provis-tos con cualquier tipo de medicamento antimalárico en 2006 fueron Botswana, las Comoras, Eritrea, Malawi,

WORLD MALARIA REPORT 2008 xix

Santo Tomé y Príncipe, el Senegal, la República Unida de Tanzanía y Zimbabwe. En ese grupo de países, Eri-trea, Santo Tomé y Príncipe y la República Unida de Tanzanía disfrutaron además de un suministro relati-vamente satisfactorio de TCA.

20. Según las encuestas nacionales de hogares, sin embar-go, ninguna de las poblaciones de los 18 países afri-canos objeto de sondeo en 2006 y 2007 tenían acceso suficiente a antimaláricos. Sólo en Benin, el Camerún, la República Centroafricana, Gambia, Ghana, Uganda y Zambia se trataba con antimaláricos a más de un 50% de los niños con fiebre. El acceso al tratamiento no alcanzó en ningún país la meta del 80%, y la media en la totalidad de los 18 países fue del 38%. El uso de TCA fue mucho menor: sólo lo recibieron un 3% de los niños por término medio, con un margen de entre el 0,1% de Gambia y el 13% de Zambia.

21. Un subconjunto de 16 encuestas nacionales de hoga-res reveló que la mayor frecuencia de tratamiento pre-ventivo intermitente (TPI, 2 dosis de sulfadoxina-pirimetamina) de las mujeres embarazadas se daba en Gambia, Malawi, el Senegal y Zambia (33%–61%), con una media del 18% de las mujeres en la totalidad de los 16 países.

22. Fuera de África, el acceso al tratamiento es más difícil de calibrar: las encuestas de hogares con preguntas sobre el tratamiento de la malaria son mucho menos frecuentes y, como ocurre con África, los programas nacionales de control no informan sobre el diagnóstico y tratamiento en el sector privado. No obstante, por lo que puede colegirse a partir de los datos de los PNCM, los países relativamente bien provistos con antimalá-ricos fueron Bhután, la República Democrática Popular Lao, Vanuatu y Viet Nam.

Financiación del control de la malaria La financiación del control de la malaria alcanzó en 2006 cotas sin precedentes, pero aún no es posible determinar, a partir de los presupuestos de los PNCM, qué países disponen de recursos suficientes para controlar la enfermedad. 23. Según los datos de los PNCM correspondientes a 2006,

la Región de África es la que disponía de más fon-dos para combatir la malaria, y la que informó de un mayor aumento de la financiación entre 2004 y 2006. Sin embargo, el total de US$ 688 millones destinados a la Región de África en 2006 representa sin duda una subestimación, pues sólo notificaron al respecto 26 de un total de 45 países. Es improbable que los US$ 4,6 disponibles por caso (estimado) de malaria en los 26 países informantes sean suficientes para alcanzar las metas de prevención y curación.

24. Las principales fuentes de fondos extraordinarios para los países africanos entre 2004 y 2006 fueron los gobiernos nacionales de los países afectados y el Fon-do Mundial de Lucha contra el SIDA, la Tuberculosis y

la Malaria. Esas dos fuentes dominaron el terreno de la financiación de la lucha antimalárica en la Región de África y en todo el mundo en 2006.

25. La distribución del apoyo financiero difirió entre las regiones de la OMS. En las regiones de las Américas, Europa y Asia Sudoriental, la mayoría de los fondos fueron aportados por los gobiernos de los países endé-micos. En las regiones del Mediterráneo Oriental y el Pacífico Occidental, el Fondo Mundial fue la principal fuente de apoyo financiero. La Región del Pacífico Occi-dental dependió más de la financiación externa, segui-da de las regiones de África y el Mediterráneo Oriental. Los países africanos son los que recibieron apoyo de una más amplia variedad de organismos externos.

Impacto del control de la malariaAlgunos países que han aplicado programas enérgicos de prevención y curación, en África y en otras regiones, han informado de reducciones importantes de la carga de malaria. 26. Aunque es posible evaluar el impacto del control de la

malaria mediante encuestas de población periódicas -sobre la prevalencia del parásito, los casos de anemia, la mortalidad específica por malaria y la mortalidad por todas las causas-, este informe se centra en las conclusiones que pueden extraerse de los informes de vigilancia nacionales.

27. Entre los 41 países africanos que proporcionaron infor-mes de casos y sobre la mortalidad durante el periodo 1997–2006, los datos probatorios más convincentes sobre el impacto proceden de cuatro países, o partes de países, con poblaciones relativamente pequeñas, una buena vigilancia y una alta cobertura de las inter-venciones. Se trata de Eritrea, Rwanda, Santo Tomé y Príncipe y Zanzíbar (República Unida de Tanzanía). Esos cuatro países/zonas lograron reducir la carga de malaria en un 50% o más entre 2000 y 2006–2007, en consonancia con las metas de la AMS.

28. En otros países africanos en que una alta proporción de la población tiene acceso a medicamentos antima-láricos o mosquiteros con insecticida, como Etiopía, Gambia, Kenya, Malí, el Níger y el Togo, los datos de la vigilancia sistemática aún no muestran de forma inequívoca las reducciones esperadas de la morbilidad y la mortalidad. Bien los datos están incompletos, o bien el efecto de las intervenciones es reducido.

29. La alta cobertura conseguida al parecer en lo referente al rociamiento de interiores con insecticidas de acción residual en Namibia, Sudáfrica y Swazilandia concuer-da con la disminución del número de casos observada en esos países, y se beneficia sin duda de los buenos resultados conseguidos antes con el RIR.

30. Los informes de vigilancia de numerosos países que no son africanos indican que la malaria disminuyó duran-te el decenio 1997–2006. Los casos de malaria han caí-

xx WORLD MALARIA REPORT 2008

do al menos en 25 países endémicos en cinco regiones de la OMS. En 22 de esos países, el número de casos notificados se redujo en un 50% o más entre 2000 y 2006–2007, en consonancia con las metas de la AMS.

31. La cifra registrada de muertes por malaria ha dismi-nuido al menos en seis países de las Américas, así como en las regiones de Asia Sudoriental y el Pacífico Occidental. Los países en cuestión son Camboya, la República Democrática Popular Lao, Filipinas, Surina-me, Tailandia y Viet Nam, todos los cuales están bien encaminados para alcanzar las metas de la AMS rela-tivas a la reducción de la mortalidad por malaria para 2010.

32. Las reducciones del número de casos y las defunciones pueden relacionarse en algunos países con interven-ciones específicas, por ejemplo con el uso focalizado de MTI en Camboya, la India, la República Democráti-ca Popular Lao y Viet Nam. Sin embargo, en general, la relación entre las intervenciones y las tendencias observadas sigue siendo dudosa, y en la mayoría de los países habrá que realizar investigaciones más meticu-losas sobre los efectos de las medidas de control.

33. La OMS distingue cuatro fases en el camino hacia la eliminación de la malaria, de tal manera que a julio de 2008 los 109 países/territorios afectados por la mala-ria se clasificaron de la siguiente manera: control (82), preeliminación (11), eliminación (10) y prevención de la reintroducción (6). En enero de 2007 los Emiratos Árabes Unidos se convirtieron en el primer país endé-mico que la OMS certificaba como libre de malaria des-de los años ochenta, lo que elevó a 92 el número total de países/territorios sin esa enfermedad.

WORLD MALARIA REPORT 2008 1

1. Introduction

1. Malaria burden and trends as reported to WHO, leading to an update of the scale of the malaria problem by country, by region and worldwide for the period 2001–2006. This report contains a complete set of estimates of malaria cases and deaths for each of the 109 malari-ous countries and territories and an assessment of the uncertainty surrounding these estimates, and draws comparisons with other estimation exercises.

2. National policies and strategies on malaria control, established in response to the burden of disease. This report details whether or not national malaria control programmes (NMPs) are implementing WHO-recommend-ed policies and strategies for malaria control, taking into account the interventions that are appropriate in different epidemiological settings.

3. Progress in implementing control measures, as compared with international targets for malaria control. Extending a 2007 UNICEF review (12), the report highlights, country by country, progress in implementing the WHO-recom-mended methods of prevention (insecticide-treated nets, indoor residual spraying, ITP) and treatment (artemisi-nin-based combination therapy and other appropriate treatment). The analysis identifies the gaps in programme implementation by intervention, geographical location and groups at particular risk in each population.

4. Funding to support malaria control, described in relation to the coverage of major interventions. National budg-ets and expenditures are given, together with sources of funding and major areas of expenditure. Although the financial data are incomplete for many countries, the report makes an initial attempt to examine whether available funding for malaria control is adequate to meet needs.

5. Recent evidence of the epidemiological impact of malar-ia control programmes, obtained both from routine surveillance and survey data.

In all five areas, the report presents a critical review of the evidence, and of the conclusions that can be drawn from it. These conclusions are presented so as to stimulate improvements in policy, financing, implementation, and monitoring and evaluation. The goal of the World malaria report, in short, is to support the development of effective national malaria control programmes.

The large, round numbers that delineate the immense and persistent burden of malaria have become a familiar part of discussions in the global public health forum: 3 billion people at risk of infection in 109 malarious countries and territories and around 250 million cases annually, leading to approximately 1 million deaths. In 2004, Plasmodium falciparum was among the leading causes of death world-wide from a single infectious agent (1).

These commonly-cited statistics have underpinned a renewed assault on malaria, which has been under way since the turn of the millennium. There is an emerging consensus on how best to use refined methods for malaria prevention and treatment, notably long-lasting insecticid-al nets (LLIN) and artemisinin-based combination therapy (ACT), backed by indoor residual praying (IRS) (2–4). More widespread agreement on policy and strategy has stimu-lated leaders of the countries most affected, backed by international organizations and donors, to set increasingly ambitious targets for control: that is, to achieve at least 80% coverage of key interventions by 2010 (5). Beyond Scaling Up For Impact (SUFI) (6), Malaria No More (7) and renewed calls for action by the UN Secretary General, there is active debate about the possibility of large-scale malar-ia elimination (8–10).

As malaria control intensifies, it is vital to monitor malaria burden and trends, and to track the coverage and impact of interventions. While malaria undoubtedly imposes a major burden on health, estimates of the num-bers of cases and deaths have been, for many countries, too inaccurate to establish firm baselines against which to evaluate the success of control measures. Therefore, while each year more people are protected against infected mosquitoes, and more have access to correct antimalarial medicines, measures of the number of people who need and who receive these services are still not sufficiently precise, either for programme planning or for evaluation against coverage targets. Most difficult of all is the assess-ment of epidemiological impact. Although malaria control programmes are not conducted as controlled experiments, there are valid methods for evaluating impact from surveil-lance and survey data. However, all such methods require accurate data.

Against that background, this second edition of the World malaria report, following the first edition published in 2005 (11), reviews progress in control in five areas.

2 WORLD MALARIA REPORT 2008

References1. Global burden of disease: 2004 update. Geneva, World Health

Organization, 2008 (www.who.int/healthinfo/bodestimates/en/index.html).

2. Malaria vector control and personal protection: report of a WHO study group. Geneva, World Health Organization, 2006.

3. Indoor residual spraying – Use of indoor residual spraying for scaling up global malaria control and elimination. Geneva, World Health Organization, 2006.

4. Guidelines for the treatment of malaria. Geneva, World Health Organization, 2006.

5. Targets for malaria control. Geneva, World Health Organiza-tion, 2008.

6. Malaria Control and Evaluation Partnership in Africa. Scal-ing Up for Impact. MACEPA model for malaria control. PATH, 2007 (www.path.org/projects/malaria_control_partnership.php).

7. Bednets for all of Africa. Malaria No More, 2008 (www.malaria nomore.org).

8. Global malaria control and elimination: report of a technical review. Geneva, World Health Organization, 2008.

9. Feachem R, Sabot O. Lancet, 2008, 371:1633.

10. Malaria forum keynote address. Bill and Melinda Gates Foun-dation, 17 October 2007 (www.gatesfoundation.org).

11. Roll Back Malaria/WHO/UNICEF. World malaria report 2005.Geneva, World Health Organization, 2005.

12. UNICEF. Malaria and children: progress in intervention cover-age. New York, United Nations Children’s Fund, 2007.


2. Policies, strategies and targets

for malaria control

The government of every country affected by malaria has a national malaria control policy covering prevention and case-management. This chapter summarizes the policies, strategies and targets for malaria control that are recom-mended by WHO.

Diagnosis and treatment of malaria, including preventive treatmentThe objectives of an antimalarial treatment policy are to: ensure rapid cure of the infection; reduce morbidity and mortality, including malaria-related anaemia; prevent the progression of uncomplicated malaria into severe and potentially fatal disease; reduce the impact of malaria infection on the fetus during pregnancy; reduce the reser-voir of infection; prevent the emergence and spread of drug resistance; and prevent malaria in travellers.

Current WHO recommendations for the diagnosis and treatment of malaria (1) are given in Box 2.1. In order to achieve global and national targets, diagnostic and treat-ment methods should follow the guidelines for deployment shown in Box 2.2.

Malaria prevention through mosquito controlThe main objective of malaria vector control is to reduce significantly the incidence and prevalence of both para-site infection and clinical malaria. There are two main approaches to malaria prevention by mosquito control: the use of insecticide-treated nets (ITNs) and indoor residual spraying (IRS). These core interventions may be comple-mented, usually in specific locations, by other methods such as larval control or environmental management. WHO recommendations for the use of ITNs and IRS are shown in Box 2.3. Additional guidance on the effective deployment of mosquito-control methods appears in Box 2.4.

Goals, indicators and targetsThe launch of Roll Back Back Malaria (RBM) in 1998, the United Nations Millennium Declaration in 2000, the Abuja Declaration by African Heads of State in 2000 (part of the African Summit on Roll Back Malaria), the World Health Assembly in 2005, and the RBM global strategic plan 2005–2015 have all contributed to the establishment of goals, indicators and targets for malaria control. The following is a brief account of the goals for malaria control, and of the way in which progress towards these goals will be measured.

BOX 2.1

WHO recommendations for the diagnosis and treatment of malaria1. The treatment of malaria infections should be based on a

laboratory-confirmed diagnosis, with the exception of children under 5 years of age in areas of high transmission in whom treatment may be provided on the basis of a clinical diagnosis.

2. All uncomplicated P. falciparum infections should be treated with an artemisinin-based combination therapy,1 and P. vivax with chloroquine and primaquine (except where P. vivax is resistant to chloroquine, when it should be treated with ACT and primaquine).

3. Four ACTs are currently recommended for use: artemether-lumefantrine, artesunate-amodiaquine, artesunate-mefloquine and artesunate-sulfadoxine-pyrimethamine. The choice of the ACT should be based on the efficacy of the partner medicine in the country or area of intended deployment.

4. Patients suffering from severe malaria presenting at the peripheral levels of the health system should be provided prereferral treatment with quinine or artemisinins, and transferred to a health facility where full parenteral treatment and supportive care can be given.

5. Severe malaria should be treated parenterally with either an artemisinin derivative2 or quinine until the patient can swallow, when a complete course of ACT must be administered.

6. In areas of high transmission, intermittent preventive treatment (IPT) with sulfadoxine-pyrimethamine (SP) should be administered to pregnant women at least twice during the second and third trimesters of pregnancy, and three times in the case of HIV-positive pregnant women. The effectiveness of IPT should be monitored in light of increasing SP resistance.

1 In central America, the only remaining region where P. falciparumis sensitive to chloroquine, the change to ACT should be made when chloroquine failure rates reach 10%.

2 Artesunate is preferred in areas of low to moderate transmission.


BOX 2.2

A guide to the deployment of diagnostic and treatment methods1. Effective diagnosis and treatment should be available at all health

facilities.2. In situations where a health facility is not accessible to the

majority of people within 24 hours of the onset of illness, diagnosis and treatment should be provided through a programme of home-based management of malaria.

3. Diagnosis and treatment may be provided through the private sector, informal and formal, but with stringent government regulation.

4. Commodities and services should be made available at affordable prices and with quality assurance.

5. A quality assurance system for both microscopy and RDT should be established. Such a system will promote a high quality of diagnosis and care, and produce reliable data for surveillance and impact assessment.

6. Prereferral treatment with artemisinins/quinine, followed by immediate referral to higher levels of the health system, should be implemented at primary health care facilities for patients with severe malaria.

7. Rectal artemisinins should be deployed at the community level as prereferral treatment of severe malaria in children under 5 years and followed up with referral to a health facility where full treatment can be provided.

8. An efficient national procurement and supply-chain management system for medicines and rapid diagnostic tests should be implemented, to ensure that commodities that meet minimum standards are available at points of delivery.

9. Pharmacovigilance is needed to detect and report adverse reactions to medicines, including the maintenance of a pregnancy registry to study the effects of artemisinins on pregnant women and on pregnancy outcomes.

10. The use of monotherapies in the treatment of P. falciparummalaria should be abandoned (2). This applies to artemisinins and to current and potential partner medicines. The purpose is to increase efficacy of treatment and, importantly, delay the onset of parasite resistance.

11. Surveillance of therapeutic efficacy over time is an essential component of malaria control. The results of in vivo tests for therapeutic efficacy provide essential information for determining whether first- and second-line drugs are still effective (3, 4).1

12. Operational research is needed to identify and address gaps in knowledge, and improve practices and delivery mechanisms. This research will assist countries in achieving the targets for curative and preventive treatment.

BOX 2.3

WHO recommendations for the use of insecticide-treated nets (ITNs) and the application of indoor residual spraying (IRS)1. Protective nets should be treated with long-lasting formulations

of insecticide (LLINs) (5). ITNs should be used by everyone in the community: high levels of net usage are more likely to protect individuals who do not use nets, in addition to those who do use them.

2. Young children and pregnant women are the most vulnerable; their protection with ITNs is the immediate priority while progressively achieving full coverage. In areas of low transmission, where all age groups are vulnerable, national programmes should establish priorities on the basis of the geographical distribution of malaria.

3. IRS is the application of insecticides to the inner surfaces of dwellings, where endophilic anopheline mosquitoes often rest after taking a blood meal (6). Twelve insecticides belonging to four chemical groups are currently recommended by WHO for IRS. The selection of an insecticide for IRS in a given area is based on data on insecticide resistance, the residual efficacy of insecticide, costs, safety and the type of surface to be sprayed.

4. DDT has comparatively long residual efficacy (6 months or more) against malaria vectors and plays an important role in the management of vector resistance. WHO recommends DDT only for indoor residual spraying. Countries may use DDT for as long as necessary, in the quantity needed, provided that the guidelines and recommendations of WHO and the Stockholm Convention are all met (7).

5. Only insecticides to which vectors are susceptible should be used. Monitoring and management of insecticide resistance are vital in the use of ITNs and IRS.

1 Routine surveillance for drug efficacy, established by NMCPs in col-laboration with WHO, has identified e.g. the failure of ACT on both sides of the Cambodia–Thailand border, perhaps caused by local emer-gence of resistance to artemisinin derivatives.


BOX 2.4

A guide to the effective deployment of mosquito-control methods1. Both IRS and ITNs may be used in a range of epidemiological

settings, from low to high transmission.2. The distribution of ITNs should be either free of charge or highly

subsidized. Mosquito nets are bulky, so special attention should be paid to procurement, storage and transport. For example, where access during the rainy season is difficult, LLINs should be prepositioned during the dry season.

3. In order to protect a high proportion of people at risk, a minimum ratio of one ITN or LLIN per two persons at risk is recommended.

4. Several years of consecutive rounds of IRS are usually required to achieve and sustain the full potential of this intervention, so the adoption of IRS requires medium– to long-term political and financial commitment by national programmes and funding partners. Therefore, IRS would ideally not be planned unless full capacity for implementation, monitoring and evaluation is in place at national, provincial and district levels.

5. Timing in IRS operations is essential. Owing to the short duration of insecticide efficacy when sprayed on walls, spraying campaigns must be completed just before the onset of the transmission season. Because they are costly, it is not usually feasible to implement IRS continuously for long periods of time.

6. IRS is effective in reducing malaria parasite prevalence and incidence in areas of high transmission but, once these goals have been achieved, IRS may be supplemented and then supplanted by other interventions, including LLINs.

7. IRS is the first-line intervention for containing malaria epidemics, and earlier application is likely to be more effective. IRS may also be used to prevent transmission in epidemic-prone areas and in areas with low seasonal transmission (e.g. highlands, fringes); in settings where LLINs are ineffective owing to pyrethroid resistance; and occasionally to control malaria in “complex emergencies” (e.g. displaced populations, refugees).

8. IRS and LLINs may be jointly deployed in areas of high transmission to further enhance their impact through extended insecticide coverage in time and space. The combination can also maintain the efficacy of vector control through the management of insecticide resistance, and limit the application of IRS in situations where it cannot be properly implemented (timing, dosage, coverage) or might be interrupted (e.g. shortage of funds, social disruption, war).

Global visionThe Roll Back Malaria partnership’s vision is that “by 2015, the malaria-related Millennium Development Goals (MDGs) are achieved. Malaria is no longer a major cause of mortal-ity and no longer a barrier to social and economic develop-ment and growth anywhere in the world”(8).

Global targetsSince the inception of accelerated malaria control with the founding of the Roll Back Malaria Partnership in 1998, the principal goal has been to reduce mortality by 50% by 2010 (9). In April 2000, African Heads of State, as part of the African Summit on Roll Back Malaria, made commit-ments “to an intensive effort to halve the malaria mortal-ity for Africa’s people by 2010”(10). In 2005, the World Health Assembly determined to “ensure a reduction in the burden of malaria of at least 50% by 2010 and by 75% by 2015” (11). This resolution has been interpreted to mean a reduction in malaria morbidity as well as mortality. The reference year for measuring changes in morbidity and mortality was taken to be 2000.

With the publication in 2005 of the Roll Back Malaria global strategic plan for 2005–2015 (9), WHO and RBM adopted 2005 as the baseline for evaluating whether mor-bidity and mortality had been reduced by 75% by 2015. The baseline year was changed from 2000 to 2005 because better data were available to make mortality estimates in 2005 (compared with 2000). In addition, malaria mortal-ity was unlikely to have changed much between 2000 and 2005, since the major impetus in malaria control in most high-burden countries began in 2005 and 2006.

The targets for coverage with curative and preventive measures were initially set at 60% of all populations at risk by 2005 (10). In 2005, the World Health Assembly increased those coverage targets to 80% by 2010 (11).

The United Nations Millennium Development Goals (MDGs) also include targets for malaria control. In 2000, the United Nations, as part of the Millennium Declara-tion, resolved by 2015 to “reduce… under-five mortality by two thirds of their current rates” (12). In addition, they resolved “to have halted and begun to reverse… the scourge of malaria…”.

In 2003, malaria experts set objectives that were more measurable, and revised these objectives in 2007. Building on that work, the 2007 United Nations Secretary-General’s report included indicators to monitor progress towards new targets, as recommended by the Inter-Agency and Expert Group on MDG Indicators (IAEG). MDG target 6C is to “have halted by 2015 and begun to reverse the incidence of malaria and other major diseases”. The indicators specific to malaria are:

6.6 Incidence and death rates associated with malaria.

6.7 Proportion of children under 5 years sleeping under insecticide-treated bednets.

6.8 Proportion of children under 5 years with fever who are treated with appropriate antimalarial drugs.


Table 2.1 Malaria indicators, targets and sources of data (9, 11, 13–15)

1. TRENDS IN MALARIA CASES AND DEATHS

IMPACT MEASURE INDICATOR NUMERATOR DENOMINATOR DATA TYPE/SOURCE TARGET

Malaria cases 1.1 Confirmed malaria Confirmed malaria cases Population (< 5 years Routine surveillance Reduction in cases andcases (microscopy or RDT, per year (< 5 years or or total) deaths per capita: per 1000 persons per total) 50% by 2010year)a compared to 2000,

and 75% by 20151.2 Inpatient malaria No. of inpatient malaria Population (< 5 years Routine surveillance compared to 2005c

cases (per 1000 persons cases per year or total)per year)b (< 5 years or total)

Malaria deaths 1.3 Inpatient malaria No. of inpatient malaria Population (< 5 years Routine surveillancedeaths (per 1000 persons deaths per year or total)per year)b (< 5 years or total)

1.4 Malaria-specific No. of malaria deaths per Population (< 5 years Verbal autopsy (surveys),deaths (per 1000 persons year (< 5 years or total) or total) in study sample complete or sample vital per year) registration systems

Used mostly for high- No. of deaths in children Population < 5 years Household surveys, transmission countries < 5 years old from all in study sample complete or sample vital1.5 Deaths in children causes registration systems< 5 years old from all causes (per 1000 children < 5 years old per year)

a Use only if > 90% of suspected cases have parasite-based examination (microscopy or RDT).b Marker for severe malaria.c Cases and deaths per capita may also be compared with the MDG baseline of 1990, although estimates for 1990 are less reliable.

2. COVERAGE OF INTERVENTIONS

CONTROL STRATEGY INDICATOR NUMERATOR DENOMINATOR DATA TYPE/SOURCE TARGET

Prompt access to 2.1 Appropriate No. of children < 5 years No. of children < 5 years Household surveys 80%effective treatment antimalarial treatment of receiving appropriate with fever in the last

children < 5 years within antimalarial treatment 2 weeks in surveyed24 hours of onset of (according to national householdsa

fevera,b,c policy) within 24 hours of(MDG indicator 6.8) onset of fever

Mosquito control with 2.2 ITN use in all persons No. of persons (all ages) No. of persons (all ages) Household surveys 80%insecticide-treated nets or children < 5 years or or children < 5 years or or children < 5 years old(ITNs) pregnant women pregnant women that or pregnant women in

(MDG indicator 6.7)d,e reported sleeping under surveyed householdsan ITN during previous night

2.3 Coverage of all No. of persons covered No. of persons at risk of Routine NMCP data, 80%persons at risk with an by ITN distributede malaria epidemiological estimatesITNd,e

Mosquito control by 2.4 Households sprayed No. of households sprayed No. of households Routine NMCP data 80%indoor residual spraying with insecticide among in one year according to targeted according toof insecticide (IRS) those targeted national guidelines national guidelines

Prevention of malaria Used mostly for high- No. of pregnant women No. of pregnant women Routine antenatal 80%in pregnancy transmission countries who received two doses with at least one ANC clinic data

2.5 Pregnant women that of intermittent visit in one yearreceived two doses of preventive treatmentintermittent preventive treatment

a As malaria incidence is reduced, a smaller percentage of fevers will be due to malaria. With improved diagnosis, treatment can be targeted at confirmed cases. This indicator is currently under review.b In areas where P. vivax is dominant, and in areas of low transmission, this indicator may be less useful.c The intention is to treat all persons with an appropriate antimalarial medicine; however, children are most at risk, especially in areas of high transmission.d Indicator should be calculated separately for all persons, children, and pregnant women.e LLINs are the preferred type of ITN; LLINs are assumed to be protective for three years. One LLIN is assumed to protect two persons.


3. OPERATIONAL INDICATORS USED AT HEALTH FACILITY, DISTRICT AND NATIONAL LEVELS, MEASURED USING ROUTINE HEALTH INFORMATION SYSTEMS

MONITORING INDICATOR NUMERATOR DENOMINATOR DATA TYPE/ SOURCE TARGET

Malaria transmission 3.1 Slide positivity No. of laboratory- No. of suspected malaria Routine surveillance No target set, indicatesrate (SPR)a confirmed malaria cases cases with parasite- level of controlb

based laboratory examination

Quality of diagnosis 3.2 Percentage of out- No. of outpatient No. of outpatient Routine surveillance 90%patient suspected malaria suspected malaria cases suspected malaria cases datacases that undergo that undergo laboratory that should be examinedlaboratory diagnosisc diagnosis (in age groups

specified by national policy)

Appropriate treatment 3.3 Percentage of out- No. of malaria cases No. of outpatient malaria Routine logistic data 100%at health facilities patient cases that receiving appropriate cases expected to be

received appropriate antimalarial treatment treated at health-facilityantimalarial treatment at health facility level with appropriateaccording to national antimalarial medicinepolicy

Routine distribution of 3.4 ITN distribution to No. of ITNs distributed Population at risk Routine logistic data 80%mosquito nets by NMCP populations at risk to populations at risk

(e.g. pregnant women attending antenatal clinics, children attending EPId clinics)

Antimalarial drug 3.5 Health facilities No. of health facilities No. of reporting health Routine logistic data 100%supplies without stock-outs of without stock-outs of facilities

first-line antimalarial first-line antimalarialmedicines, mosquito nets medicines, ITN and RDT, and diagnostics, by by monthmonth

Reports for programme 3.6 Completeness of No. of reports received No. of reports expected Routine surveillance and > 90%management monthly health facility each month, on logistics each month logistic data

reports on logistics or or surveillancesurveillance

a “Slide” includes microscopy or rapid diagnostic test (RDT).b SPR < 5% during the malaria season marks the transition from control stage to pre-elimination stage.c Laboratory diagnosis includes microscopy and RDT; this is also an indicator of the quality of surveillance. d Expanded Programme on Immunization.

Drawing together the work of Roll Back Malaria since 1998, the Abuja Declaration in 2000, the 2005 World Health Assembly, the various revisions of MDGs specifically for malaria, and the global strategic plan, Table 2.1 shows the recommended indicators (with numerators and denomina-tors) for measuring epidemiological impact (part 1) and effective coverage (part 2), plus the targets for 2010 and 2015. MDG indicators 6.7 and 6.8 are aligned with indica-tors in Table 2.1, namely 2.2 and 2.1. In addition, Table 2.1 (part 3) gives operational indicators that are useful for managing national malaria programmes at health facility, district and national levels.

References1. Guidelines for the treatment of malaria. Geneva, World Health

Organization, 2006.

2. Resolution WHA60.18. Malaria, including proposal for estab-lishment of World Malaria Day. Geneva, World Health Organi-zation, 2007 (60th World Health Assembly, 23 May 2007).

3. Assessment and monitoring of antimalarial drug efficacy for the treatment of uncomplicated falciparum malaria. Geneva, World Health Organization, 2003.

4. Field application of in vitro assays for the sensitivity of human malaria parasites to antimalarial drugs. Geneva, World Health Organization, 2007.

5. Long-lasting insecticidal nets for malaria prevention. A manu-al for malaria programme managers (trial edition). Geneva, World Health Organization, 2007.

6. Indoor residual spraying – Use of indoor residual spraying for scaling up global malaria control and elimination. Geneva, World Health Organization, 2006.

7. The use of DDT in malaria vector control. WHO position state-ment. Geneva, World Health Organization, 2007.

8. What is the Roll Back Malaria (RBM) Partnership? Roll Back Malaria, 2008 (http://www.rbm.who.int/aboutus.html).


9. Global strategic plan 2005–2015. Roll Back Malaria, 2008 (http://www.rbm.who.int/forumV/docs/gsp_en.pdf).

10. Roll Back Malaria/WHO. The Abuja Declaration and the plan of action. An extract from the African Summit on Roll Back Malaria, Abuja, 25 April 2000. Geneva, World Health Organi-zation, 2000 (WHO/CDS/RBM/2000.1; http://www.rbm.who.int/docs/abuja_declaration.pdf).

11. Resolution WHA58.2. Malaria control. Geneva, World Health Organization, 2005 (58th World Health Assembly, 23 May 2005 (http://www.who.int/gb/ebwha/pdf_files/WHA58/WHA58_2-en.pdf).

12. Millennium Development Goals Indicators. New York, United Nations, 2008 (http://millenniumindicators.un.org/unsd/mdg/Host.aspx?Content=Indicators/About.htm).

13. Roll Back Malaria. Framework for monitoring progress and eval-uating outcomes and impact. Geneva, World Health Organiza-tion, 2000.

14. Roll Back Malaria/MEASURE Evaluation/WHO/UNICEF. Guide-lines for core population coverage indicators for Roll Back Malar-ia: to be obtained from household surveys. MEASURE Evalua-tion, 2004.

15. Global malaria indicators and their measurement. Geneva, World Health Organization, 2007.


3.Estimated burden of malaria in 2006

The methods used to estimate the populations at low andhigh risk of malaria are described in the country profiles:methods and definitions (page 35). The methods used tocalculate the numbers of cases and deaths, by country andregion, are described in Annex 1. Estimates for each coun-try in 2006 are in Annex 2.

Population at riskThe 109 countries and territories classified as endemic, orpreviously endemic with the threat of reintroduction, fallinto four groups. The four groups describe the transitionfrom control to elimination (Fig. 3.1, chapter 5).

Information on the number of people living in areaswhere malaria transmission occurs was obtained from

NMCPs. About half the world’s population (3.3 billion)live in areas that have some risk of malaria transmissionand one fifth (1.2 billion) live in areas with a high risk ofmalaria (more than 1 reported case per 1000 populationper year; Table 3.1). Another 2.1 billion live in areas oflow risk. Although low-risk areas cover a large number ofpeople living across a wide geographical area, they pro-duce a relatively small number of malaria cases each year(less than 2 million) and account for less than 3% of casesreported by countries in 2006. The largest populations atany risk of malaria are found in the WHO South-East Asiaand Western Pacific regions. Africa has the largest numberof people living in areas with a high risk of malaria, fol-lowed by the South-East Asia Region (Fig. 3.2).

Fig. 3.1 Malaria-free countries and malaria-endemic countries in phases of control, pre-elimination, elimination and prevention ofreintroduction, end 2007

Certified malaria-freeand/or no ongoinglocal transmissionfor over a decade

Prevention ofreintroduction

Elimination

Pre-elimination

Control

a China, Indonesia, Phlippines, Solomon Islands, Sudan, Vanuatu and Yemen have subnational elimination programmes.

10 World malaria report 2008

Malaria cases estimates of the number of malaria cases were made by: (1) adjusting the reported malaria cases for reporting com-pleteness, the extent of health service utilization and the likelihood that cases are parasite-positive*; or (2) from an empirical relationship between measures of malaria trans-mission risk and case incidence (Annex 1).

there were an estimated 247 million malaria cases (5th–95th centiles, 189–327 million) worldwide in 2006, of which 91% or 230 million (175–300 million) were due to P. falciparum (Table 3.1). the vast majority of cases (86%) were in the african region, followed by the South-east asia (9%) and eastern mediterranean regions (3%) (Fig. 3.3). the percentage of cases due to P. falciparum exceeded 75% in most african countries but only in a few countries outside africa (Fig. 3.4).

in africa, 19 of the most populous countries accounted for 90% of estimated cases in 2006 (Fig. 3.5). there is consid-erable uncertainty surrounding the estimates for individual countries. For example, lower and upper estimates for Niger-ia (35–80 million cases), derived from risk mapping, differ by a factor of more than two. estimates for Kenya, derived from routine surveillance reports, ranged from 5 million to 19 million cases in 2006, a fourfold difference.

Table 3.1 Estimates of populations at low and high risk of malaria, and estimates of cases and deaths compared with NMCP reports, by WHO region, 2006

POPulaTiON aT risk (MilliONs) POPulaTiON % aNy risk TOTal aT risk lOW risk HigH risk HigH risk (as % Of aNy risk)

Africa 774 84 647 61 586 91

Americas 895 15 137 76 61 45

Eastern Mediterranean 540 55 295 230 66 22

Europe 887 2 22 19 2 11

South-East Asia 1 721 77 1 319 863 457 35

Western Pacific 1 763 50 888 833 54 6

World 6 581 50 3 308 2 082 1 226 37

CasEs (THOusaNds) rEPOrTEd % falCiParuM EsTiMaTEd lOWEr uPPEr rEPOrTEd/EsTiMaTEd (%)

Africa 83 618 98 212 000 152 000 287 000 36

Americas 1 042 29 2 700 2 400 3 200 39

Eastern Mediterranean 2 914 76 8 100 7 000 11 400 84

Europe 2 2 4 4 5 63

South-East Asia 4 338 56 21 000 19 000 29 000 20

Western Pacific 2 133 67 2 200 1 500 3 200 95

World 94 048 92 247 000 189 000 327 000 37

dEaTHs (THOusaNds) rEPOrTEd rEPOrTEd EsTiMaTEd lOWEr uPPEr rEPOrTEd/EsTiMaTEd (%) (all agEs) (% < 5 yEars) (all agEs)

Africa 156 88 801 529 1 126 20

Americas 0 29 3 2 3 8

Eastern Mediterranean 2 76 38 20 60 5

Europe 0 0 0 0 0 0

South-East Asia 2 35 36 24 50 5

Western Pacific 1 40 4 2 6 33

World 161 85 881 610 1 212 18

fig. 3.2 Number of people estimated to be at low and high risk of malaria, by WHO region, 2006

Popu

lation

at r

isk (m

illion

s)

0

500

1000

1500

sEar WPr afr EMr aMr Eur

Low risk (<1 case/1000/year)High risk (>=1 case/1000/year)

* Cibulskis re et al. estimating trends in the burden of malaria. American Journal of Tropical Medicine and Hygiene, 2007, 77(suppl 6): 133–137.

World Malaria report 2008 11

Fig. 3.3 Estimated incidence of malaria per 1000 population, 2006

0–4

5–49

50–200

>200

Fig. 3.4 Estimated percentage of malaria cases due to P. falciparum, 2006

0–24%

25–49%

50–74%

75–100%


Ten countries accounted for 90% of the cases thatoccurred outside Africa in 2006, with wide uncertaintyintervals around the point estimates (Fig. 3.6). Approxi-mately 30% of these cases, and 15% of P. falciparum cases,were in India.

The number of cases reported by national malaria con-trol programmes (NMCPs) was only 37% of the estimatedglobal incidence. The gap between case reports and trueincidence was greatest in the South-East Asia Region, andleast in the Eastern Mediterranean and Western Pacificregions (Fig. 3.7). The gap between reported and esti-mated cases needs careful interpretation (Box 3.1). Forexample, a high ratio of reported/estimated cases doesnot necessarily mean that a high proportion of cases iscaptured by a national reporting system.

Malaria deathsEstimates of the number of malaria deaths were also madeby: (1) multiplying the estimated number of P. falciparummalaria cases by a fixed case-fatality rate for each country;or (2) from an empirical relationship between measuresof malaria transmission risk and malaria-specific mortalityrates (Annex 1).

There were an estimated 881 000 (610 000–1 212 000)deaths worldwide in 2006, of which 90% were in the AfricanRegion, and 4% in each of the South-East Asia and EasternMediterranean regions (Table 3.1). The risk of death frommalaria is considerably higher in Africa than other parts ofthe world (Fig. 3.8). An estimated 85% of deaths occur inchildren under 5 years, but the proportion is much higherin the African (88%) and Eastern Mediterranean regions(76%) than in other regions (16–40%, Table 3.1). Eight-een countries accounted for 90% of deaths in the AfricanRegion (Fig. 3.9), and seven countries had 90% of deathsoutside the African Region, dominated by Sudan and India(Fig. 3.10).

According to data and estimates, only 1 in 5 malariadeaths was reported worldwide in 2006, but the ratio ofreported/estimated deaths was much lower in the Ameri-cas and in the Eastern Mediterranean and South-East Asiaregions (Table 3.1). These low ratios are seen in somecountries outside Africa where NMCPs rely solely on malar-ia-specific information systems, and do not compile datafrom other inpatient and death registration systems. Inaddition, reports of malaria deaths may appear to be morecomplete in Africa because deaths, like cases, are not usu-ally confirmed parasitologically.

CommentOur calculations of the number of people at risk of malariadiffer from other recent estimates by Guerra et al. (1),where 1.4 billion people were estimated to be at high riskof falciparum malaria (as distinct from all Plasmodium spp)and 0.97 billion were at low risk (Table 3.1).

Some of the differences between the new estimatespresented here and those of Guerra et al. could be dueto variations in the completeness and quality of the data

Fig. 3.5 Nineteen countries estimated to have 90% of cases inthe African Region, 2006a

a The width of bars indicates 5th and 95th centiles.

RwandaAngola

ZambiaGuinea

ChadMali

MalawiCameroon

NigerBurkina FasoCôte d'Ivoire

GhanaMozambique

UgandaKenya

United Republic of TanzaniaEthiopia

Democratic Republic of the CongoNigeria

0 20 40 60 80 100

Estimated number of malaria cases (millions)

Fig. 3.6 Ten countries estimated to have 90% of cases in regionsother than Africa, 2006a

a The width of bars indicates 5th and 95th centiles.

Sudan

Estimated number of malaria cases (millions)

Afghanistan

Somalia

Brazil

Pakistan

Papua New Guinea

Indonesia

Bangladesh

Myanmar

India

0 5 10 15

Fig. 3.7 Reported cases as a percentage of all estimated cases,by WHO region, 2006

WPR EMR EUR AMR AFR SEAR

Repo

rted

case

s as a

per

cent

age o

fes

timat

ed ca

ses

0

20

40

60

80

100


BOX 3.1

Reported versus estimated malaria cases

Fig. 3A Percentage of surveillance reports obtained byNMCPs, by WHO region, 2006

% of

coun

tries

with

stat

ed re

porti

ng fr

actio

n

0

20

40

60

80

100

EUR WPR EMR SEAR AFR AMR

< 50% 50–80% >80%

Fig. 3B Sources of treatment for malaria patients,by WHO region, 2006

% m

alaria

case

s see

king t

reat

men

t

0

20

40

60

80

100

AFRO AMRO SEARO EURO EMRO WPRO

No treatment Private sector Public sector

Fig. 3C Percentage of suspected malaria cases in publichealth facilities that were given a laboratorydiagnostic test, by WHO region, 2006

Perc

enta

ge of

coun

tries

EUR AMR WPR SEAR EMR AFR0

20

40

60

80

100

Cases examined (%):0–19% 20–39% 40–59% 60–79% 80–100%

Fig. 3D Distribution of slide positivity rates reported bycountries, by WHO region, 2006

Num

ber o

f cou

ntrie

s

0

10

20

30

40

50

AFR AMR EMR EUR SEAR WPR

0–9%10–19%20–29%30–39%40–49%50%+

There are three factors that determine whether malaria cases reported byNMCPs are a true reflection of the number of cases occurring in a country(Annex 1):

(1) The completeness of reports in routine surveillance systems. Inreply to a WHO questionnaire, many NMCPs claimed that the percentageof reports submitted by health facilities was greater than 80% of thenumber expected. Reporting was most complete in the European andWestern Pacific regions (Fig. 3A). About half of the countries in theAfrican Region stated that reporting rates were above 80%.

(2) The proportions of malaria patients that use public and privatehealth facilities, or do not seek treatment at all. From an analysis ofhousehold survey data, it is estimated that 37% of malaria cases soughttreatment at facilities covered by ministry of health reporting systems(Fig. 3B; see also chapter 4). The remaining 63% of cases use facilitiesin the private sector, shops and pharmacies, or do not seek treatmentat all. The South-East Asia Region had the lowest percentage of casescaptured by ministry of health reporting systems, primarily because alarge number of patients use private practitioners of one sort or another.

(3) The proportion of cases that have a confirmed diagnosis. In somecountries, mainly in regions other than Africa, all suspected malariacases are subjected to laboratory investigation, and all reported malariacases have confirmed Plasmodium infections (Fig. 3C). In many Africancountries, a small proportion of suspected malaria cases is subjected tolaboratory investigation, and diagnosis is based only on clinical signs andsymptoms. Because slide positivity rates are generally below 50%, morethan half of all clinically diagnosed cases do not have malaria (Fig. 3D).

These three factors may together generate counterbalancing errorsin reported cases; for example, while overdiagnosis in (3) leads tooverestimates of incidence, failure to take into account cases attendingprivate health facilities (2) leads to underestimates. All three factors mustbe considered when estimating the total number of cases in a country.


Fig. 3.8 Estimated deaths from malaria per 1000 population, 2006

0–0.14

0.15–0.24

0.25–0.49

0.5–5

Fig. 3.9 Eighteen countries estimated to have 90% of malaria deaths in the African Region, 2006

Number of malaria deaths (000s)

MalawiZambiaGuinea

ChadMozambiqueCôte d'Ivoire

AngolaCameroon

MaliGhana

Burkina FasoKenyaNiger

United Republic of TanzaniaEthiopiaUganda

Democratic Republic of the CongoNigeria

0 100 200 300 400

Fig. 3.10 Seven countries estimated to have 90% of malaria deaths in regions other than Africa, 2006

Number of malaria deaths (000s)

Papua New Guinea

Indonesia

Somalia

Bangladesh

Myanmar

India

Sudan

0 10 20 30 40 50 60

used in the two assessments. However, the larger estimate of the number of people at high risk obtained by Guerra et al. is partly explained by their choice of a lower threshold of 1 case per 10 000 people per year to delimit low and high risk areas. populations living in areas with a risk of 1 case per 5 000 would be classified as high risk by Guerra et al. that previous study found a smaller number of peo-ple at low risk, partly because of the lower threshold, but also because their estimates were restricted to falciparum malaria. the new calculations presented here include P. vivax. in particular, they take account of the extensive geographical distribution of P. vivax in China. this inclusive approach is likely to overestimate the number of people at any risk of malaria because transmission becomes more patchy in areas of low incidence. to obtain more accurate estimates of risk, the distribution of malaria needs to be mapped on a fine scale, to populations of 100 000 or less.

the population at risk of malaria is, in areas of moder-ate or high transmission, a useful guide to the number of people who would benefit from insecticidal nets (itNs) or indoor residual spraying (irS), but these risk estimates are less useful in areas of low transmission where preventive methods are used more focally.

the estimates of malaria incidence in africa given in Annex 1 and Table 3.1 are similar to those made by Snow et al. (210 million cases, inter-quartile range 130–325 million cases (2))1 and Korenromp (230 million cases (3)). this is to be expected because the data and estimation procedures were similar. Snow et al. later provided a higher estimate of

1 Note that the estimate for the WHo african region excludes djibouti, Somalia and Sudan.


365 million cases in Africa (4), using an updated endemic-ity map, but the range (IQR 216–374 million) still overlaps with range of estimates for P. falciparum here.

Incidence estimates given here for regions other than Africa are substantially lower than previously estimated by Mendis et al. (51.2 million) (5) and Snow et al. (150.1 million) (4). Previous estimates are for the mid-1990s, or employ endemicity maps derived from an even earlier peri-od, and some of the differences could be due to real reduc-tions in the number of cases since that time. However, the estimation methods used in previous publications have also been challenged; for example, some calculations (3)have given numbers for countries in the Western Pacific Region that are considered by other authorities to be too high (6).

Whichever combination of data and calculation methods is used, current estimates of malaria cases and deaths are surrounded by much uncertainty. The imprecision affects the estimates for each country, as well as the ranking of countries in regions.

For most countries in Africa, the number of cases and deaths was derived from approximate relationships between transmission intensity, malaria case incidence and malaria-specific mortality, based on limited data (method 2). In regions other than Africa, and for selected African countries, incidence estimates were based on rou-tine surveillance data (method 1). This is the preferred method for all countries because it accounts for the vari-ety of factors that influence incidence and mortality from year to year. Such information can also be readily incor-porated into the planning and evaluation of malaria pro-grammes. However, the estimates are critically dependent on the information provided to WHO by NMCPs, and on data collected in published household surveys. Estimates of the number of malaria cases are particularly sensitive to the completeness of health facility reporting. If health ministries keep accurate records of the number of surveil-lance reports received and expected from health facilities, then adjustments can be made for missing reports. How-ever, if this information is not recorded accurately then the adjustments made could be inappropriate leading either to an over- or underestimate of the number of cases. The systems of disease surveillance and vital registration that provide essential data are often at their weakest in the countries most affected by malaria.

Estimates of the number of deaths in Africa are within the range estimated by Snow et al. (1.1 million deaths, inter-quartile range 700 000–1.6 million (2)). Estimates of the number of deaths are also broadly consistent with those obtained in the 2004 Global Burden of Disease study.1 The main difference is that the new estimates include fewer deaths in the Western Pacific Region, principally because of the apparent reductions in mortality in Cambodia and Viet Nam (chapter 5). Mortality estimates for countries and regions include the same kinds of uncertainty as observed for case incidence estimates.

References1. Guerra CA et al. The limits and intensity of Plasmodium fal-

ciparum transmission: implications for malaria control and elimination worldwide. PLoS Medicine, 2008, 5(2):e38.

2. Snow RW et al. The public health burden of Plasmodium fal-ciparum malaria in Africa: deriving the numbers. Bethesda, M.D., Fogarty International Center/National Institutes of Health, 2003 (The Disease Control Priorities Project (DCPP) Working Paper Series No. 11).

3. Korenromp E. Malaria incidence estimates at country level for the year 2004. Geneva, World Health Organization, 2005 (draft) (www.malariaconsortium.org/resources.php?action= download&id=177).

4. Snow RW et al. The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature, 2005, 434:214–217.

5. Mendis K et al. The neglected burden of Plasmodium vivax malaria. American Journal of Tropical Medicine and Hygiene,2001, 64(1-2 Suppl):97–106.

6. Bell DR et al. Malaria risk: estimation of the malaria burden. Nature, 2005, 437(7056):E3–4; discussion E4–5.

1 www.who.int/healthinfo/bodestimates/en/index.html.


4.Interventions to control malaria

Adoption of policies and strategies for malaria controlVector control Table 4.1 and Annex 4a show the number of countries in each region that had adopted nine key policies and strate-gies for malaria control recommended by WHO. Most coun-tries in the WHO African Region adopted, by the end of 2006, the policy of providing ITNs free of charge (33) to children and pregnant women (37), but only 16 aimed to cover the whole population at risk. Insecticidal nets are used in a high proportion of countries in the South-East Asia and Western Pacific regions, typically with free dis-tribution to the whole population at risk. ITNs are used in relatively few countries in the other three WHO regions.

IRS is the dominant method of vector control in the European Region (8 of 9 endemic countries), with the greatest number of houses sprayed in Azerbaijan, Tajikistan and Turkey. It is used in fewer countries in the African (16 of 45) and South-East Asia regions (6 of 10) and in the Americas (12 of 22), and least in the Western Pacific Region (2 of 10).

There was little variation among the six WHO regions in the proportion of countries that said they had a strategy for managing insecticide resistance, ranging from 40% to 60% of countries in each region. A total of 53 countries and

territories, among the 109 with malarious areas, claimed to have a strategy for resistance management.

Diagnosis and case managementWorld Health Assembly resolution WHA60.18 (May 2007) urged Member States to discourage or disallow the use of oral artemisinin-based monotherapies, in both the public and private sector; to promote the use of artemisinin-based combination therapies; and to implement policies that prohibit the production, marketing, distribution and use of counterfeit antimalarial medicines.1

Since 2001, a growing number of countries have adopt-ed the policies for case management recommended by WHO. Fig. 4.1 shows the steep upward trend in the number of countries worldwide that adopted ACT as the first-line treatment for P. falciparum (dark bars), albeit with a time lag until deployment of these medicines in the general health services (light bars).

ACT was the first-line treatment for P. falciparum in 66 countries by the end of 2006, and in almost all countries in the African, South-East Asia and Western Pacific regions. By June 2008 (the latest information available to WHO), only 4 countries and territories worldwide (Cape Verde, Dominican Republic, French Guyana and Swaziland) had

1 Further information on policies related to drug use can be found on the following web sites: www.who.int/malaria/treatmentpolicies.html;www.who.int/malaria/pages/performance/marketingmonotherapies.html; www.who.int/malaria/pages/performance/monotherapycountries.html.

Table 4.1 Number of countries having adopted WHO-recommended policies and strategies for malaria control, by WHO region

WHO REGION ITN IRS TREATMENT IPT

ITN- IT-TARGETING ITN- IRS AS IRS- DDT USED ACT TREATMENT- IPT STRATEGY NO.TARGETING CHILDREN DISTRIBUTION- PRIMARY INSECTICIDE- FOR IRS (YES/NO) ACT IS USED TO ENDEMIC

ALL UNDER FREE VECTOR RESISTANCE FREE IN PREVENT COUNTRIES5 YEARS AND CONTROL MANAGEMENT PUBLIC MALARIA

PREGNANT METHOD IMPLEMENTED SECTORS DURING WOMEN PREGNANCY

Africa 16 37 33 16 23 8 40 23 33 45

Americas 12 6 5 12 9 1 8 6 0 22

Eastern Mediterranean 6 4 7 5 6 0 10 7 0 13

Europe 3 3 4 8 5 0 1 1 0 9

South-East Asia 9 6 9 6 6 3 9 8 0 10

Western Pacific 8 4 7 2 4 1 9 6 0 10

Total 54 60 65 49 53 13 77a 51 33 109Data are as reported by NMCPs at the end of 2006 except for policy on ACT treatment, which has been updated to June 2008.a Out of 81 countries endemic for P. falciparum.


not yet adopted ACT as the first-line treatment for P. falci-parum (Table 4.1, Annex 4b). Free treatment with ACT ismore widely available in the South-East Asia and WesternPacific regions than in the African Region.

Among the four recommended drug combinations thatinclude artemisinin derivatives (chapter 2), WHO is cur-rently monitoring the global supply of and demand for thefixed-dose combination artemether-lumefantrine (AL), asone of the requirements of a memorandum of understand-ing signed by the manufacturer Novartis and WHO in 2001,which makes Coartem® available at cost price through pub-lic health services. During 2006 and 2007, most AL wasprocured for young children below 15 kg (Fig. 4.2). Thehighest proportions of countries procuring AL in 2007 werein the African (23 of 45) and South-East Asia (5 of 10)regions. There were fewer in the Region of the Americas(2 of 22), and in the Eastern Mediterranean (4 of 13) andWestern Pacific regions (2 of 10).

UNICEF and other agencies (Crown Agents, IDA Solu-tions, John Snow Incorporated, Medical Export Group,Médecins sans Frontières, Missionpharma, United NationsDevelopment Programme, United Nations Office for ProjectServices) have established direct procurement agreementswith Novartis to supply Coartem® at the price negotiatedby WHO. Consequently, a declining proportion of AL wasprocured through WHO by 2007, as other agencies played alarger role (Fig. 4.3).

By 2007, a total of 40 out of 74 private companies iden-tified by WHO had declared their intention to stop produc-ing and marketing oral artemisinin-based monotherapies.However, only 20 of 78 countries had introduced regula-tory measures that will lead to the withdrawal of mono-therapy.

The quantity of antimalarial drugs needed in each coun-try depends on whether treatment is given presumptively toall patients who present with fever, or whether malaria andthe species of Plasmodium are confirmed by parasitologicaldiagnosis. The proportion of people treated for malaria thathave a confirmed diagnosis is low in the African Regioncompared with other regions of the world (Fig. 4.4), withthe result that antimalarials, where they are available,could be used to treat patients without malaria.

Fig. 4.1 Worldwide adoption and deployment of ACT as first-linetreatment for P. falciparum

Num

ber o

f cou

ntrie

s

0

20

40

60

80

2001 2002 2003 2004 2005 2006 2007 2008

Adopted ACT as policyDeployed ACT

Fig. 4.2 Procurement of artemether-lumefantrine, by patientbody weight (orders placed in 2005–2007)

5–14 15–24 25–34 35+

Dose

s of A

L (m

illion

s)

0

5

10

15

20

25

2005 2006 2007

Body weight (kg)

Fig. 4.3 Procurement of artemether-lumefantrine,by procurement agency, 2005–2007

Dose

s of A

L (m

illion

s)

0

10

20

30

40

50

60

2005 2006 2007

UNICEF WHO Others

Fig. 4.4 Persons treated for malaria that have aconfirmed diagnosisa

a The percentage is calculated as: (persons positive by microscopy or RDT)/(persons positive bymicroscopy or RDT + reported malaria cases not examined by microscopy or RDT).

AFR WPR EMR SEAR AMR EUR

Mala

ria p

atien

ts w

ith co

nfirm

ed d

iagno

sis (%

)

0

20

40

60

80

100


Intermittent preventive treatment in pregnancythe systematic use of ipt in pregnancy is restricted to the african region where 33 of the 45 countries had adopted ipt as national policy by the end of 2006 (Table 4.1).

Coverage of interventionsDistribution, possession and use of insecticidal netsData from national malaria control programmes the number of itNs (other than lliNs) distributed annually appears to have increased in the african, South-east asia and Western pacific regions in 2005 and 2006 (Fig. 4.5a). But the most striking change in the african region is in the number of lliNs distributed from 2005 onwards, reach-ing 36 million in 2006 (Fig. 4.5b). as a result of this sharp increase, 70% of all nets distributed by NmCps in africa in 2006 were lliNs.

assuming that one itN is needed for every two people at risk (as recommended by WHo), approximately 324 (647 divided by 2) million itNs (preferably lliNs) were needed in the african region in 2006. NmCps reported that 52.9 million lliNs were distributed between 2004 and 2006, and 13.2 million itNs in 2006 (Annex 5). assuming that each lliN is effective for three years and any other itN for one year, it may be estimated that 66.2 million people were protected in 2006.

While the recent increase in lliN distribution in africa is impressive, there is enormous variation in the availabili-ty of nets among countries. itNs protected more than 20% of people at risk of malaria in only 19 african countries in 2006, and more than 50% in only 6 countries (Fig. 4.6).

eight countries completed nationwide distribution of lliNs between 2004 and 2007. ethiopia (2005–2006) and Zambia (2006–2007) targeted all households. togo (2004), Niger (2005–2006), rwanda (2006), Kenya (2006), Sierra leone (2006) and mali (2007) distributed lliNs primarily to children under 5 years and pregnant women. all except mali and togo appear in Fig. 4.6.

in regions other than africa, itNs are commonly tar-geted not at the entire population at any risk of malaria, but towards subpopulations at higher risk. For this reason, only nine NmCps outside africa reported itN distribution that could have protected more than 10% of the popula-tion at risk in 2006, all in the South-east asia and Western pacific regions (Fig. 4.7). in 28 countries that reported on itN use in 2006, the numbers of nets distributed would have protected an average of 4% of the population living in areas with any risk of malaria.

Data from household surveysin 2006 and 2007, 22 countries carried out surveys of mosquito net ownership and use, 18 in the african region (Annex 6). the 18 african countries were inhabited by 276 million people, 43% of the african population at risk of malaria. Surveys carried out in 2007 in the democratic republic of Congo, Nigeria and rwanda were unpublished at the time of completing this report.

in the surveyed african countries, an average of 34%

Fig. 4.5 Distribution of (a) ITN other than LLIN and (b) LLIN by national malaria control programmes, in the African, South-East Asia and Western Pacific regions

(a)

(b)

2001 2002 2003 2004 2005 2006

2001 2002 2003 2004 2005 2006

Num

ber o

f ITN

exce

pt LL

IN (m

illion

s)Nu

mbe

r of L

LIN (m

illion

s)

0

5

10

15

0

10

20

30

40

AFR SEAR WPR

AFR SEAR WPR

Fig. 4.6 Nineteen African countries with ITN sufficient to cover > 20% of the population at risk in 2006–2007 (NMCP data)

Num

ber I

TN in

relat

ion to

pop

ulat

ion a

t risk

(%)

0

20

40

60

80

100

Mad

agas

car

Com

oros

Mala

wi

Guin

ea-B

issau

Buru

ndi

Togo

Ghan

a

Liber

ia

Gam

bia

Rwan

da

Nam

ibia

Sier

ra Le

one

Gabo

n

Mad

agas

car

Keny

a

Zam

bia

Ethi

opia

Nige

rSa

o Tom

e & P

rincip

e


(mean weighted for population at risk) of householdsowned an ITN (18 countries). Only 23% of children sleptunder an ITN (18 countries), as compared with the targetof 80% (chapter 2). In a smaller subset of 8 countries, anaverage of 27% of pregnant women slept under an ITN.

As indicated by NMCP data (above), there was wide vari-ation among countries in ITN possession and use. The per-centage of households that owned an ITN was greater thanor equal to 40% in 8 countries in 2006–2007 (Fig. 4.8a).In the 18 surveyed countries, there was no significant dif-ference in the proportion of households that owned an ITNin rural (26% on average) and urban areas (34%).

There were only six countries in which more than 30%of children under 5 years slept under an ITN (Ethiopia,Gambia, Guinea-Bissau, Niger, Sao Tome and Principe, andTogo), and only Niger reported that more than 50% of chil-dren slept under an ITN on the night preceding the survey(Fig. 4.8b). The percentage of women sleeping under anITN in the 8 surveyed countries ranged from 10% in Ugan-da to 48% in Niger (Fig. 4.8c).

While these surveys reveal that the coverage of ITNs inAfrican populations was nowhere near the target of 80% by2006, consecutive surveys in selected countries show howhousehold ownership (Fig. 4.9a) and usage of ITNs (Fig.4.9b) increased over the preceding 6–7 years, notably inEthiopia, Gambia, Guinea-Bissau, Sao Tome and Principe,Togo and Zambia.

Only four countries outside the African Region didhousehold surveys in 2006 (Annex 6). An ITN was ownedby 18% of households in Djibouti, 12% in Somalia, 18% inSudan and 19% in Viet Nam. The proportions of childrenthat slept under an ITN were different, but also low: 1%in Djibouti, 9% in Somalia, 28% in Sudan and 5% in VietNam.

Indoor residual spraying of insecticideInformation about indoor residual spraying of insecticidecomes from national malaria control programmes; IRS cov-erage is not recorded in household surveys.

According to NMCP data, more than 100 million peo-

Fig. 4.7 Nine countries in the Americas, South-East Asia andWestern Pacific with ITN sufficient to cover > 10% ofthe population at risk in 2006–2007 (NMCP data)

Num

ber I

TN in

relat

ion to

pop

ulat

ion a

t risk

(%)

0

10

20

30

40

50Sr

i Lan

ka

Cam

bodia

Suda

n

Lao P

DR

Papu

a Ne

w G

uine

a

Solom

on Is

lands

Surin

ame

Vanu

atu

Bhut

an

Fig. 4.8 Household surveys of (a) ITN ownership, (b) use bychildren < 5 years and (c) pregnant women, Africa,2006–2007 (DHS, MICS and MIS surveys)

(a)

Côte

d'Iv

oire

Ugan

da

Ghan

a

Cam

eroo

n

Burk

ina

Faso

Beni

n

Ango

la

Sao T

ome &

Prin

cipe

Sene

gal

Mala

wi

Togo

Guin

ea-B

issau

Zam

bia

Gam

bia Mali

Ethi

opia

Nige

r

Hous

ehold

s with

at l

east

1 IT

N (%

)

100

Cent

ral A

frica

n Re

publi

c

target > 80%

0

20

40

60

80

100

Côte

d'Iv

oire

Burk

ina

Faso

Ugan

da

Cam

eroo

n

Sene

gal

Ango

la

Beni

n

Ghan

a

Zam

bia

Mala

wi

Mali

Ethi

opia

Togo

Guin

ea-B

issau

Sao T

ome &

Prin

cipe

Gam

bia

Nige

r

Child

ren

who s

lept u

nder

ITN

(%)

(b)

target > 80%

Cent

ral A

frica

n Re

publi

c

0

20

40

60

80

100

Uganda Senegal Benin Angola Zambia Mali Ethiopia Niger

Preg

nant

wom

en w

ho sl

ept u

nder

ITN

(%)

(c)

target > 80%

0

20

40

60

80

100


ple were protected by IRS in 2006, including 70 million inIndia and 22 million in the African Region. IRS coveragevaried greatly among countries other than India. ElevenAfrican countries that use IRS provided information oncoverage in 2006. In 9 of these, coverage would have beensufficient to protect at least 10% of the population at risk,and coverage was estimated to be over 70% in Botswana,Namibia, Sao Tome and Principe, South Africa and Swazi-land (Fig. 4.10a).

Outside the African Region, the use of IRS tends to bemore focal. Eleven countries in the other five WHO regionsreported IRS coverage that would have protected more than5% of the population at risk in 2006 (Fig. 4.10b). Cover-age above 20% was restricted to Bhutan and Suriname.

Diagnosis and case managementData from national malaria control programmesNMCPs reported the worldwide distribution of 16 millionRDTs, 80 million courses of antimalarial medicine, and 49million courses of ACT through public health services in2006. The total number of treatment courses of antima-larial drugs delivered through public health services were32% of an estimated 247 million cases. The reported vol-ume of antimalarial drugs distributed increased abruptlyfrom 2004 onwards, but the large increase in ACT distri-bution started in 2006 (Fig. 4.11). These reports fromcountries undoubtedly underestimate ACT usage: ordersby, and deliveries to, countries for just one of the fourACTs recommended by WHO (i.e. artemether-lumefantrine)is known from other sources to have exceeded 67 millioncourses of treatment in 2006. Most of the other antimalar-ial medicines (besides ACT) are procured by wholesalers forprivate-sector distribution and these data are not avail-able to NMCPs.

As with insecticidal nets, most of the drugs were report-edly distributed in a few countries in 2006. Consideringdrug supplies in relation to estimated malaria cases, as ameasure of potential demand, the African countries best-provisioned with any antimalarial drugs in 2006 were Bot-swana, Comoros, Eritrea, Malawi, Sao Tome and Principe,Senegal, United Republic of Tanzania and Zimbabwe, all ofwhich had more than one dose per case (Fig. 4.12a). Forthe provision of ACT, only Eritrea, Sao Tome and Principe,Uganda and the United Republic of Tanzania had more thanone dose per case (Fig. 4.12b).

Prior to 2006, ACT was used to treat significant num-bers of cases in just a few African countries, for exampleBurundi (1.2 million courses in 2005), Ethiopia (3.2 millionin 2005) and Kenya (723 000 in 2005). Zambia and Zanzi-bar (United Republic of Tanzania) started ACT distributionin 2004, but did not report on their distribution or usagebefore 2006.

In regions other than Africa, 12 countries distributedmore than one treatment course of antimalarial drugs perestimated case through public health services. Drug sup-plies in relation to malaria cases were greatest in Bhu-tan, Lao People’s Democratic Republic, Papua New Guinea,

Fig. 4.9 Increases in (a) household ownership of ITN and(b) the proportion of children using ITN in Africancountries with at least 2 surveys (DHS, MICSand MIS surveys)a

a The limits of the bars show the the coverage at earlier (left) and later (right) surveys.

(a)

Households with an ITN (%)

(b)

Children < 5 who slept under an ITN (%)

0 10 20 30 40 50 60

Côte d'Ivoire 2005–06

Ghana 2003–06

Cameroon 2004–06

UR Tanzania 1999–2004

Burkina Faso 2003–06

Senegal 2005–06

Malawi 2004–06

Zambia 2001–06

Ethiopia 2000–07

Sierra Leone 2000–05

Cote d'Ivoire 2000–06

Burundi 2000–05

Burkina Faso 2003–06

Uganda 2000–06

Rwanda 2000–05

Cameroon 2004–06

Central African Republic 2000–06

UR Tanzania 1999–2004

Senegal 2000–06

Benin 2001–06

Ghana 2003–06

Zambia 1999–2006

Malawi 2000–06

Ethiopia 2005–07

Togo 2000–06

Guinea-Bissau 2000–06

Sao Tome & Principe 2000–06

Gambia 2000–06

Niger 2000–06

0 10 20 30 40 50 60


Vanuatu and Viet Nam (Fig. 4.13). Nicaragua and Turkeyreported drug stocks greatly in excess of apparent need(Annex 5).

Ten African countries reported that a total of 10.8 millionRDTs were distributed in 2006. The total was partitionedvery unevenly among Ethiopia (9.7 million, more than 90%of the distribution in the African Region), Angola (4.9 mil-lion) and Sierra Leone (1.5 million), with fewer in Burundi,Eritrea, Kenya, Mauritania, Sao Tome and Principe, Senegaland Uganda,.

Of the 4.8 million RDTs used outside Africa, more thanhalf were used in India (2.8 million), and fewer than 1 mil-lion in each of Bangladesh, Indonesia, Lao People’s Demo-cratic Republic, Myanmar, Papua New Guinea and Somalia.

Data from household surveysEighteen national household surveys were carried out inthe African Region in 2006–2007. They found that lessthan half (average 38%) of children under 5 years withfever took an antimalarial drug, and 19% took an antima-larial on the same or the next day. Just 3% of children inthe 18 countries were given ACT (at any time), and 18%

Fig. 4.10 Countries in (a) the African and (b) other regionswhere > 5% of the population at risk was protectedby IRS in 2006a

(a)

0

20

40

60

80

100M

adag

asca

r

Eritr

ea

Ethi

opia

Zam

bia

Moz

ambiq

ue

Zimba

bwe

Bots

wana

Sao T

ome &

Prin

cipe

Swaz

iland

Sout

h Af

rica

Nam

ibia

Popu

lation

at r

isk p

rote

cted

by I

RS (%

)

(b)

0

10

20

30

40

Uzbe

kista

n

Azer

baija

n

Para

guay

Suda

n

Iran

Sri L

anka

Solom

on Is

lands

Geor

gia

Beliz

e

Surin

ame

Bhut

an

Popu

lation

at r

isk p

rote

cted

by I

RS (%

)

a Estimates for India, Namibia and South Africa were > 100% of the population at risk.

Fig. 4.11 Rapid diagnostic tests and antimalarial drugs distributedby public health services, 2001–2006 (NMCP data)

Num

ber o

f cou

rses

of tr

eatm

ent (

milli

ons)

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006

RDT ACT Any antimalarial (inc ACT)

Fig. 4.12 Doses of (a) any antimalarial drug and (b) ACT, perestimated case of malaria, distributed by NMCPs in theAfrican Region in 2006–2007 (NMCP data)

(a)

Dose

s of a

ntim

alaria

l dru

gs p

er ca

se

0

5

10

15

Ethi

opia

Nige

r

Buru

ndi

Burk

ina

Faso

Zam

bia

Sier

ra Le

one

Sene

gal

Sao T

ome &

Prin

cipe

UR Ta

nzan

ia

Com

oros

Zimba

bwe

Mala

wi

Eritr

ea

Bots

wana

Rwan

da

Togo

Com

oros

Keny

a

Ango

la

Ghan

a

Ethi

opia

Liber

ia

Sene

gal

Buru

ndi

Zam

bia

Sier

ra Le

one

Eritr

ea

Sao T

ome &

Prin

cipe

Ugan

da

UR Ta

nzan

ia

(b)

0

0.5

1

1.5

2

2.5

Dose

s of A

CT p

er ca

se


Fig. 4.13 Doses of any antimalarial drug, per case of malaria,distributed by NMCPs in regions other than Africa in2006–2007 (NMCP data)

Dose

s of a

ntim

alaria

l dru

gs p

er ca

se

0

5

10

15

20

25Ch

ina

DPR

Kore

a

Pana

ma

Yem

en

Nepa

l

Geor

gia

Cost

a Ri

ca

Papu

a Ne

w G

uine

a

Vanu

atu

Bhut

an

Lao P

DR

Viet N

am

Child

ren

< 5

year

s tre

ated

with

antim

alaria

l (%

)

0

20

40

60

80

100

Ethi

opia

Sene

gal

Mala

wi

Sao T

ome &

Prin

cipe

Ango

la

Mali

Nige

r

Côte

d'Iv

oire

Guin

ea-B

issau

Togo

Burk

ina

Faso

Beni

n

Cent

ral A

frica

n R

epub

lic

Cam

eroo

n

Zam

bia

Ghan

a

Ugan

da

Gam

bia

target > 80% Any antimalarial (inc ACT) ACT

Fig. 4.14 Availability of any antimalarial drug and ACT to children with fever,2006–2007 (DHS, MICS and MIS surveys)

of pregnant women in 16 countries used IPT ( 2 doses ofSP). These averages are much lower than the 80% targetfor all treatment indicators.

Access to treatment varied widely among the Africancountries surveyed. Treatment with any antimalarial drugtaken at any time ranged from 10% in Ethiopia to 63%in Gambia, and with ACT from 0.1% in Gambia to 13% inZambia (Fig. 4.14). Besides Zambia, only Sao Tome andPrincipe provided ACT to more than 5% of children withfever. The use of IPT by pregnant women in 16 countriesvaried from 0.3% in Niger to 61% in Zambia (Fig. 4.15).

Outside the African Region in 2006, only three countriesundertook national household surveys (MICS) that includ-ed questions on antimalarial treatment. These few surveysfound that the percentages of children < 5 years with feverwho took any antimalarial drugs were 10% in Djibouti, 8%

Somalia and 3% in Viet Nam.Access to antimalarial drugs is typi-

cally better in urban than rural areas.In 41 surveys carried out worldwidebetween 2000 and 2007 (37 in the Afri-can Region), the proportion of childrenunder 5 years treated with any antima-larial medicine on the same or the nextday was on average 27% higher in urbanthan rural areas (t = 5.7, p < 0.001; Fig.4.16).

While ACT has gained wider usagesince 2005, other antimalarial drugs havebeen available for much longer. Among11 African countries that carried out sur-veys over the period 2000–2001 and then

again in 2006, chloroquine use declined in 10 as expected(Fig. 4.17a). More surprisingly, the use of any antimalarialdrugs also fell in 10 out of 13 countries (Fig. 4.17b). Bycontrast, treatment with SP did not systematically increaseor decrease over this period.

Household surveys that assess the way in which malariapatients use public and private health services, used incombination with routine surveillance data, can help todefine the role of NMCPs in national malaria control (Fig.4.18). The proportion of patients with fever suspected ofbeing malaria (including fever, and fever with cough andrapid breathing) using the private sector was relativelyhigh in the South-East Asia and Eastern Mediterraneanregions (78% and 62%, respectively, of all those seekingtreatment), and low in the European (19%) and Americanregions (34%). In the African and Western Pacific regions,treatment was divided almost equally between the publicand private sectors.

Funding for malaria controlAccording to NMCP data for 2006, the African Region hadmore money for malaria control than any other, and report-ed a greater increase in funding than any other regionbetween 2004 and 2006 (Fig. 4.19; Annex 7). However,the total of US$ 688 million for the African Region in 2006

Fig. 4.15 Pregnant women who used IPT ( 2 doses ofSP/Fansidar; DHS, MICS, and MIS surveys)

Preg

nant

wom

en w

ho u

sed

IPT (

%) target > 80%

0

20

40

60

80

100

Nige

r

Burk

ina

Faso

Ango

la

Beni

n

Mali

Cam

eroo

n

Guin

ea-B

issau

Côte

d'Iv

oire

Cent

ral A

frica

nRe

publi

cUg

anda

Togo

Ghan

a

Gam

bia

Mala

wi

Sene

gal

Zam

bia


is certainly an underestimate because reports were sub-mitted by only 26 of 45 countries. The US$ 4.6 availableper (estimated) malaria case in the 26 reporting countriesis unlikely to be adequate to meet targets for preventionand cure.

Also, some countries outside Africa did not report onfunding: 9 of 22 malarious countries in the Americas, 5 of13 countries in the Eastern Mediterranean Region, and onefrom each of the European and Western Pacific regions didnot report. Based on NMCP data, the picture of funding formalaria control worldwide is therefore incomplete.

The major sources of extra funds for African countriesbetween 2004 and 2006 were reported to be nationalgovernments plus the Global Fund to Fight AIDS, TB andMalaria (Fig. 4.20a). These two sources dominated fundingfor malaria control worldwide in 2006, and in the AfricanRegion (Fig. 4.20b), but the balance of funding supportwas different in other parts of the world. In the Americas,the European and South-East Asia regions, the majorityof funds were from the governments of endemic countries(Fig. 4.21). In the Eastern Mediterranean and WesternPacific regions, the Global Fund was reported to be theprincipal source of financial support. The Western PacificRegion placed greatest reliance on external funding, fol-lowed by the African and Eastern Mediterranean regions.Countries in the African Region presented the most diverseportfolio of support from external agencies.

CommentBetween 2004 and 2006, there were marked increases infunding, and consequently in the supplies of long-lastinginsecticidal nets (LLINs) and artemisinin-based combi-nation therapy (ACT), especially in the African Region.These increases have almost certainly continued into 2007,although WHO does not yet have complete data for thatyear. The growth in supply and usage of LLINs and ACT hasfollowed the widespread adoption of policies recommendedby WHO. However, the absolute levels of funding, and the

Fig. 4.16 Comparison of access to antimalarial drugs in urbanand rural areas, 2000–2007a

Child

ren

with

feve

r give

n an

y ant

imala

rial d

rug,

urba

n ar

eas (

%)

0

10

20

30

40

50

60

70

Children with fever given any antimalarial drug, rural areas (%)

0 10 20 30 40 50 60 70

Urban = 1.27 x rural

a The indicator measured is the percentage of children < 5 years with fever who took anyantimalarial drugs the same or next day (42 DHS, MICS and MIS surveys worldwide, 38 in theAfrican Region).

Fig. 4.17 Decline in the availability of (a) chloroquine and(b) any antimalarial drugs between 2000–2001 and2006 (DHS, MICS and MIS surveys)

(a)

Child

ren

who t

ook c

hlor

oqui

ne (%

)

0

20

40

60

80

Mala

wi

Mali

Nige

r

Cam

eroo

n

Gam

bia

Côte

d'Iv

oire

Guin

ea-B

issau

Beni

n

Togo

Sao T

ome &

Prin

cipe

Cent

ral A

frica

n R

epub

lic

2000–2001 2006

0

20

40

60

80(b)

Child

ren

who t

ook a

ny a

ntim

alaria

l(%

)

Ethi

opia

Mala

wi

Sene

gal

Nige

r

Gam

bia

Côte

d'Iv

oire

Zam

bia

Guin

ea-B

issau

Togo

Beni

n

Cam

eroo

n

Sao T

ome &

Prin

cipe

Cent

ral A

frica

n R

epub

lic

2000–2001 2006

Fig. 4.18 Percentage of patients with fever that seek treatmentin public and private health facilities, and who do notseek any treatment, by WHO region (data from 59 DHSand MICS surveys, weighted by estimated fever casesper country)

Perc

ent o

f mala

ria p

atien

ts (%

)

0

20

40

60

80

100

AFR AMR EMR EUR SEAR WPR

No treatment Private Public

Fig. 4.19 Funds available for malaria control, by WHO region,2004–2006 (US$ millions, NMCP data)a

AFR EMR EUR SEAR AMR WPR

US$

milli

ons

0

200

400

600

800

2004 2005 2006

24

27

26

8 88

8 8 89 9

1017 18

13 9 9 9

a The numbers of countries submitting reports are given above each bar.


Fig. 4.21 Sources of funding for malaria control,by WHO region, 2006

AFR AMR

EMR EUR

SEAR WPR

Government Global Fund World Bank Bilateral organizations

UN agencies European Union Other

Fig. 4.20 Sources of funding for malaria control (a) in the African Region and (b) globally, 2004–2006

total supply of the commodities required for preventionand cure, were reported to be low on average and highlyvariable among countries. The approach to interpretingthe data differs between Africa and other regions, and thefollowing commentary also makes that distinction.

WHO African RegionIn the African Region, NMCP data suggest that an averageof 26% of people living in 37 countries was protected byITNs in 2006, with especially high coverage in Ethiopia,Niger and Sao Tome and Principe. Surveys in 18 countries(all except 1 carried out in 2006) found that one third(34%) of households owned an ITN, and that one quarterof children (23%) and pregnant women (27%) slept underan ITN (Table 4.2). As for the NMCP data, surveys recordedthe highest coverage in Ethiopia, Niger, and Sao Tome andPrincipe. The Roll Back Malaria Partnership estimates thatan additional 35.2 million LLINs were dispatched to coun-tries in the African Region in 2007, and that nets covereda population of 125 million, which suggests that up to 39%of people at risk could have been protected during thatyear (1). ITNs probably protected more people in 2007 thanever before, but there was a clear and large gap betweenprovision and need.

IRS is typically focal, even in Africa (2). Mozambiqueand Zimbabwe covered an estimated one third to one halfof the population at risk, but much higher coverage wasachieved in Botswana, Namibia, Sao Tome and Principe,South Africa and Swaziland.

This review of drug availability in the African Regionalso highlights the gap between provision and need. In2006, only eight countries in the African region had morethan one treatment course of antimalarial drugs per esti-mated malaria case, and only four countries had at leastone treatment course of ACT per malaria case. As meas-ures of access to treatment, these figures are no morethan indicative, because many suspected malaria casesare unconfirmed and treated presumptively and becauseantimalarial drugs are widely available outside the publichealth services, from pharmacies, shops, private practi-tioners and other outlets (private-sector data generallynot available to NMCPs). However, the impression thatdrug supplies were not sufficient in most countries is rein-forced by household surveys done in 2006 and 2007. These

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Table 4.2 Intervention coverage in the WHO African Region, as compared with targets of > 80%, based on household surveys

CONTROL STRATEGY INDICATOR COVERAGE (%) NO. COUNTRIES

Prompt access 2.1 Appropriate antimalarial 38% (any antimalarial, any time) 18to effective treatment of children 19% (any antimalarial same or 19treatment < 5 years within 24 hours of next day)

onset of fever 3% (ACT, any time) 13

Mosquito control 2.2 ITN ownership or use, 34% (households with ITN) 19with insecticidal all by children < 5 years or 23% (children < 5 years) 18nets (ITN) pregnant women 27% (pregnant women) 8

Prevention of 2.5 Pregnant women that 18% 16malaria in received two doses of inter-pregnancy mittent preventive therapy

surveys found that 38% of children with fever were given any antimalarial drug from any source, and only 3% were treat-ed with ACT.

Given the high frequency of chloro-quine resistance, it is not surprising that the use of this drug diminished over the period 2000–2001 to 2006. It is more surprising that treatment with any anti-malarial drug also dropped over this 5–6 year period. The household surveys offer one possible explanation: that the supply of alternative drugs (including ACT) was, by 2006, still inadequate to compensate for progressive chloroquine disuse.

Supplies of ACT to African countries are, however, bound to increase. The reported number of courses of ACT distrib-uted in 2006 (43 million) is lower than suggested by data from manufacturers and the main international procure-ment agencies, and RBM estimates that approximately 100 million doses of ACT were procured in 2006 (1). The dif-ference between procurement and use can be explained by the time delay from orders in 2006 to delivery in 2007.

As with the distribution of mosquito nets, there was much variation in access to drug treatment among coun-tries. While more than half the children in seven coun-tries were treated with any antimalarial drug in 2006, no national household survey has yet found that 80% or more of children with fever were given appropriate drug treatment. For ACT, Zambia was among the first African countries to distribute these drugs in 2004, and yet only 13% of children were treated with ACT in 2006. And access to treatment differs, not just between countries, but also within countries. For example, household surveys show that a higher proportion of children receive antimalarial treatment in urban than in rural areas.

In summary, measures of intervention coverage based on routine data and household surveys give a consistent picture for the African Region. The evidence presented here suggests, as in another recent review (3), that most countries were far from meeting the targets for preven-tion and cure by the end of 2006. However, while summary statistics capture the gap between supply and need (Table 4.2), a small group of African countries has performed well on all indicators. ITN coverage is relatively good in Ethio-pia, Kenya, Niger, Gambia, Madagascar, Mali, Sao Tome and Principe, and Zambia. IRS coverage is comparatively good in southern Africa (Botswana, Namibia, South Africa and Swaziland), and in Sao Tome and Principe. While access to ACT remains low almost everywhere, countries that are relatively well-provisioned with drugs include Botswana, Eritrea, Gambia, Sao Tome and Principe, Uganda, United Republic of Tanzania and Zambia. Some of these countries now have evidence that these interventions are starting to reduce malaria cases and deaths (chapter 5).

Regions other than AfricaIn regions other than Africa, the effective coverage of interventions is harder to judge because prevention, either with ITNs or IRS, is often targeted to subpopulations at relatively high risk, rather than to everyone who might be exposed to malaria. The size of these targeted popu-lations is not reported by NMCPs. In addition, the pre-cise role of NMCPs in malaria control is not always clearly defined. For example, many patients do not seek diagnosis and treatment through public health services, especially in the South-East Asia Region. One difficulty in monitoring is that household surveys are done infrequently outside the African Region; in 2006 surveys that asked questions about coverage of antimalarial interventions were done only in Djibouti, Somalia, Sudan and Viet Nam.

Working therefore with NMCP data, ITN coverage was found to be relatively high (> 20% of the population at risk) in Bhutan, Papua New Guinea, Solomon Islands and Vanuatu. Bhutan and Solomon Islands also had compara-tively high coverage of IRS, as well as Suriname (> 20% of the population at risk). And Bhutan, Lao People’s Demo-cratic Republi, Nicaragua, Turkey, Vanuatu and Viet Nam were best provisioned with antimalarial drugs (an estimate of at least four doses per case).

Based on surveys, ITN ownership was low (< 20% of households) as expected in Djibouti, Somalia and Sudan, but it was also low in Viet Nam (19%). Similarly, the pro-portions of children with fever that were given antima-larial treatment was 10% or less in Djibouti, Somalia and Viet Nam.

Some of the countries that reported high coverage of interventions (e.g. Bhutan, Lao People’s Democratic Republic and Viet Nam) have also reported falling numbers of malaria cases and deaths. The inference, to be explored further in chapter 5, is that malaria control has had a sig-nificant impact on disease burden in these countries.

The analysis in this chapter is based on routine logis-tic and surveillance data from NMCPs, on household sur-veys, and on estimates of populations at risk and disease burden, all of which have their limitations. For example, many NMCPs do not yet have reporting systems that dis-tinguish between procurement and delivery of ITNs, drugs and other commodities. Household surveys, although typi-cally well designed and executed, cannot be done annually


in all countries. And most surveys are carried out during the dry season, for logistic reasons, when ITN usage is likely to be lower than in the wet season. A 2006 survey in Niger, for example, found marked seasonal variation in ITN use by children under 5 years, from 15% in the dry season to 56% in the wet season (4). Estimates of populations at risk of malaria, which are used to judge the coverage of IRS and ITN, may give an unduly pessimistic view of malaria control in countries where insecticidal nets and residual spraying are targeted only at high-risk populations.

Funding malaria controlRecognizing the importance of linking the coverage of interventions with programme finances, Annex 7 com-piles the first set of annual reports on funding available to NMCPs. Much information is missing from countries; there are uncertainties in the submitted data and discrepancies in information from different sources (e.g. Global Fund and World Bank as compared with NMCPs; Annex 7). These prob-lems arise partly because NMCPs have no standard method of reporting on budgets and expenditures. Among other things, these data do not yet allow analysis of: (1) wheth-er a budget meets the needs for malaria control in any country; (2) whether funds have been successfully raised against that budget; and (3) whether available funds have been effectively spent according to the proposed budget.

A cautious interpretation does, however, provide some initial insights into the financing of malaria control. Fund-ing increased substantially in the African Region between 2004 and 2006, but was probably still far below need in 2006. Malaria control in the Americas and in the European and South-East Asia regions was funded mainly by the gov-ernments of the affected countries; and countries in the African, Eastern Mediterranean and Western Pacific regions placed greatest reliance on external support. These sum-mary results, and the reports for specific countries, are subject to confirmation with a more detailed analysis of verified data.

References1. Secretariat of the Roll Back Malaria Partnership and Com-

modity Services Team. Procurement of insecticidal nets by country. Geneva, Roll Back Malaria Partnership, 2008.

2. Implementation of indoor residual spraying of insecticides for malaria control in the WHO African Region. Brazzaville, WHO Regional Office for Africa, 2007.

3. Malaria and children: progress in intervention coverage. New York, United Nations Children’s Fund, 2007.

4. Thwing J et al. Insecticide-treated net ownership and usage in Niger after a nationwide integrated campaign. Tropi-cal Medicine and International Health, 2008, 13: 827–834 (2008).


5.Impact of malaria control

Evaluating malaria control from routine surveillance data Where national malaria control programmes have achieved high population coverage with one or more interventions, marked reductions in cases and deaths are expected.

While the impact of malaria control can be evaluated, with greater or lesser precision, by repeated population surveys – of parasite prevalence (1), malaria-specific mor-tality (2), or all-cause mortality (3) – this brief overview draws on information from national surveillance reports. Although routine surveillance data are highly variable in quality, the annual records of cases and deaths submitted to WHO are the most abundant source of information on the effects of malaria control worldwide. Their potential in evaluation studies has not been fully exploited.

In this chapter, the nationally aggregated surveil-lance data are used to address three questions, country by country: (1) whether there is a significant time trend; (2) whether the trend represents underlying changes in incidence or mortality; and (3) whether the trend can be attributed to one or more specific interventions. These questions are most easily answered where case and death reports are more comprehensive and submitted with fewer delays, where a higher proportion of suspected cases is confirmed by laboratory diagnosis.

The data are of two types. The first consists of reports of numbers of outpatients and inpatients (suspected and confirmed), of laboratory tests carried out on these patients (RDT, slide positivity rate, Plasmodium spp), and of malaria deaths (Annexes 1 & 3), mainly for the peri-od 2001–2006. Most of this information is available for most endemic countries. The second type, available in this report for selected African countries, specifies the timing and scale of interventions in relation to, and as a possi-ble explanation for, changes in the numbers of cases and deaths. Because malaria interventions are carried out by national public health services, and not carried out as con-trolled experiments, the plausibility of the link between interventions and malaria trends varies from one setting to another. There at least two other possible explana-tions for temporal changes in the numbers of cases and deaths: variation in reporting completeness, and changes in climatic or other environmental variables that influence malaria transmission.

Impact of malaria control in the WHO African RegionThe reported number of malaria outpatients increased steadily from 3.2 million in 2001 to 8.4 million in 2006. Approximately half (21 or 22 in each year) of the countries in the African Region reported inpatient cases to WHO between 2001 and 2006. Hospital admissions for malaria in these countries doubled from 1 million to 2.2 million over the same period, and malaria deaths rose from 100 504 to 258 548. The growth in cases and deaths, which is most likely due to improved surveillance or more complete records for recent years, is conspicuous in Benin, Burkina Faso, Ghana, Mozambique, Nigeria, Senegal and the United Republic of Tanzania. In Burkina Faso, Nigeria and Senegal, there were contemporary increases in the numbers of all outpatients attending health facilities, which points to improvements in reporting.

Because few African countries had achieved high cover-age of interventions by 2006 (chapter 4), reductions in the malaria burden across the whole region are not expected. However, within the regionwide rise in reported cases and deaths, the following six countries/areas showed declines in morbidity and mortality, with more or less supporting evidence to suggest that the reductions were due to spe-cific interventions.

EritreaEritrea had a population of 3.8 million in 2001 and reported a total of 126 000 malaria cases in that year. By 2001 the number of reported cases had already fallen to half the number reported in 1998 (255 000), although the most recent phase of malaria control did not begin until 2000. Approximately 818 000 nets were distributed from 2001 to 2006, with LLIN distribution starting in 2005. In 2004, 73% of households in areas of high transmission owned an ITN and 58.6% of children 0–5 years slept under a net (4). About 250 000 courses of antimalarial medicines are dis-tributed each year, and annual rounds of IRS have protected approximately 200 000 people between 2001 and 2006.

The number of patients admitted to hospitals for any rea-son increased by 38% between 2001 and 2006, but malaria inpatients of all ages declined by 64%, and the number of children under 5 years by 65% (Fig. 5.1a). Likewise, the number of malaria outpatients of all ages had fallen by more than 90% in 2006, and the number of reported cases in children under 5 years by 33%. Malaria deaths


(inpatients) in adults and children were approximately80% lower in 2006. Despite a doubling of the number ofpatients tested between 2001 and 2006, laboratory-con-firmed malaria cases declined by 33% over this period. Theslide positivity rate also dropped, from 43% in 2001 to14% in 2006.

Previous, fuller investigations of malaria control in Erit-rea contend that the observed declines in cases and deathswere due to these interventions and not solely to environ-mental or other factors (4–6).

MadagascarMadagascar had a population of 17 million people in 2001,of which half were at high risk of malaria. The NMCP reported1.4 million cases in that year. Three million ITNs (1.6 mil-lion in 2006) were distributed between 2001 and 2006, andanother 3 million were distributed in 2007. Approximately250 000 houses, protecting nearly 1.3 million people, weresprayed each year in 2005–2007. No information is avail-able on the recent history of treatment.

Fig. 5.1 Trends in malaria cases (inpatients and outpatients) and deaths (inpatients) in relation to interventions, six African countries,2001–2006 (NMCP data)

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As vector control has become more intensive, the totalnumber of malaria cases reported annually has fallen. Thenumber of cases reported in 2007 was less than half theaverage for 2001–2003. The number of malaria inpatients,and the number of inpatients that died, were also 40–50%lower in 2007 than in 2001–2003 (Fig. 5.1b). However, thetotal number of confirmed cases reported from Madagascarand the slide positivity rate have remained stable since2002, and it is possible that the downward trends in Fig.5.1b are due to a deterioration in inpatient reporting.

RwandaTwo sources of data were available from Rwanda: nation-wide case records for 2001–2006, as reported to WHO; anddata from a special WHO study on the impact of malariacontrol in 2001–2007, based on information from 19 healthfacilities.

Approximately 765 000 ITNs (not LLINs) were distribut-ed between 2001 and 2005 in a population of 8–9 million;185 000 LLINs were added in 2005. During a nationwide


malaria control campaign targeting children under 5 years in 2006, a further 1.96 million LLINs were distributed. As a result, 60% of children were sleeping under an ITN by 2007 (assessed by household survey in that year). In 2006, 684 990 courses of ACT were distributed for the first time. Both LLINs and ACT were rapidly distributed nationwide during September–October 2006.

The number of outpatients attending health facilities for any reason doubled between 2001 and 2006. Suspected malaria cases (seen as outpatients) increased by 42% and laboratory-confirmed malaria cases by 50% over the same period. The number of people admitted to hospital for any reason increased by 31%, and malaria inpatients by 10%. Malaria deaths dropped by 45% during this time but deaths from all causes also fell, by 20%. Therefore, surveillance data indicated limited or no impact prior to the rapid, mass distribution of LLINs and ACT in September and October 2006.

However, malaria cases and deaths appeared to decline rapidly after the distribution of LLINs and ACT (Fig. 5.1c). In the 19 health facilities visited for the special study in 2007, malaria outpatients (laboratory-confirmed) and inpatients had declined by 58% and 55%, respectively, as compared with the average for 2001–2005. Whether the decline seen in the 19 health facilities reflects a trend nationwide needs to be confirmed by a more thorough examination of all surveillance data compiled up to 2007.

Sao Tome and Principe The population of Sao Tome and Principe was 152 000 in 2006. IRS covered approximately 30 000 houses, protect-ing nearly 130 000 people in 2006 and in 2007 (chapter 4). The NMCP reported distributing 79 000 LLINs in 2005 and 2006, enough to protect almost everyone. The amount of ACT distributed (15 000–20 000 courses per year) should have been enough to cover all reported cases in 2006.

Compared to the average for 2001–2003, the number of confirmed malaria cases had been reduced by more than 80% by 2006 (Fig. 5.1d). Similarly, the number of malaria deaths reported by hospitals (inpatients) in 2006 was more than 90% lower than the previous maximum of 211 in 2002. The slide positivity rate declined from 51% in 2001 to 26% in 2005, and then to 9% in 2007. Sao Tome and Principe therefore shows a strong association between intervention and impact, albeit on a relatively small scale.

Zambia In the context of malaria control, Zambia is unique for at least four reasons: (1) health information records from health facilities in all districts have been more or less complete since 2000; (2) a 2006 survey compared parasite prevalence and anaemia among those with and without ITNs; (3) a special study compiled data during the malaria season in the first two quarters of 2007; and (4) as part of a renewed malaria control programme, ACT has been avail-able nationwide since 2004 (7).

Zambia reported 4.7 million malaria cases in 2006, a

relatively high incidence of 758 per 1000 population. Dur-ing 2002–2005, 1.26 million ITNs were distributed, enough to protect about 2.5 million people (assuming one net pro-tects two people). An additional 1.2 million LLINs were distributed in 2006. The total of 2.4 million ITNs could have protected approximately 5 million people, or one third of the population in 2006. These data are consistent with the results of a 2006 survey which found that 44% of households owned an ITN, and 23% of children under 5 years slept under an ITN. IRS covered an average of 0.9 million persons between 2003 and 2005, and 2.4 million in 2006 (mostly in urban areas). During 2004–2006, 6.5 mil-lion ACT were distributed, enough to treat approximately half of the malaria cases reported through public health facilities.

Surveillance data submitted to WHO show that the num-bers of malaria inpatients and deaths were 22% and 29% lower, respectively, in 2006 than the average for 2001–2003 (Fig. 5.1e). Both cases and deaths were falling at an average of 9% per year between 2001 and 2006. The numbers of inpatients and deaths in the first two quarters of 2007 were 31% and 37% lower, respectively, than the numbers reported in the first two quarters of 2000–2002.

These observations are consistent with the results of a 2006 national survey which found that children living in households with at least two mosquito nets had a lower prevalence of parasites and anaemia than children living in households with no ITN (1).

Zanzibar (United Republic of Tanzania)Zanzibar is unique because surveillance data, plus a special study of malaria control, both suggest that a single inter-vention – the distribution of ACT through public health facilities – began to reduce malaria across the island from 2003 onwards.

ACT was made freely available in all public health facili-ties in September 2003. One round of indoor residual spray-ing was carried out in 2006, followed by a further two rounds in 2007, both covering nearly all households. Inpa-tient malaria cases and deaths declined substantially between 2003 and 2006 (Fig. 5.1f). The slide positivity rate also declined, from 36% in 2001 to 3% in 2006. By 2006, cases and deaths had been reduced by more than 80% in comparison with the numbers recorded in 2001 and 2002 (Fig. 5.1f). However, it is uncertain if the decline in malaria admissions and deaths reported nationally reflects a true decline in malaria incidence because the number of admissions and deaths from causes other than malaria fol-lows a similar trend, which suggests a deterioration in reporting. More substantial evidence for an impact of malaria intervention comes from a detailed investigation carried out in North A District where, in children under 5 years, there were substantial reductions in P. falciparumprevalence, malaria-related admissions, blood transfu-sions, crude mortality and malaria-attributed mortality (8).


Impact of malaria control in other regionsSurveillance reports for many countries outside Africa indi-cate that malaria declined during the decade 1997–2006.At least 25 countries across the five WHO regions (exclud-ing Africa) show downward trends in malaria cases (Fig.5.2), and malaria deaths appear to have declined in atleast six countries in the Americas and in the South-EastAsia and Western Pacific regions (Fig. 5.3).

The evidence that these downward trends representreal declines in malaria burden, which can be attributedspecifically to malaria control, varies from one setting toanother. Surveillance reports for most of the countries notshown in Fig. 5.2 do not clearly indicate downward trendsin case incidence, for at least three possible reasons: thesurveillance data are unreliable (large, unexplained annualfluctuations); surveillance has been steadily improving(more cases reported each year); and the true malaria inci-dence is either steady or increasing. Which of these expla-nations applies is generally unknown.

In the WHO Region of the Americas (AMRO/PAHO), the

Fig. 5.2 Trends in reported malaria cases in selected countries, by WHO region, 1997–2006 (NMCP data)

(a) Americas (high incidence) (b) Americas (low incidence)

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Fig. 5.3 Trends in reported malaria deaths, six countries in theAmericas and the South-East Asia and Western Pacificregions, 1997–2006 (NMCP data)

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1 www.paho.org/english/ad/dpc/cd/malaria.htm.


reduction in malaria in some countries (Figs 5.2a & 2b) is coincident with improved regionwide policies on malaria control. this region1 advocates surveillance and early case detection to prevent and contain epidemics; integrated vector management; prompt diagnosis and treatment; and health system strengthening.

there is no evidence of a decline in malaria in the three countries that report the greatest numbers of cases. in Bolivia, the number of malaria cases recorded by the NMCp increased between 1997 and 2006. despite Brazil’s success in malaria control during the 1990s (6), the number of cases reported annually between 1997 and 2006 fluctuated around an average of approximately 500 000. a similar pattern was reported by Colombia, although the trend has been downwards since 2000.

in the WHo eastern Mediterranean region, the coun-tries that have shown the greatest reductions in malaria (Fig. 5.2c) are those where NMCps have strong political and financial support from the government, and which operate within health systems that are well developed at central and peripheral levels.1 the evidence of any effect of malaria control is weakest in the six higher-burden countries: afghanistan, djibouti, pakistan, Somalia, Sudan and Yemen.

in the WHo european region, turkey has reported the greatest number of cases since 1990, exceeding 80 000 in 1994, mainly from the south-east of the country along the borders with iraq and the Syrian arab republic. the rise and fall of reported cases in iraq, the Syrian arab republic and turkey run in parallel, and appear to be linked epidemiologically. large-scale epidemics of malaria in Cen-tral asia, particularly in tajikistan, followed the break-up of the former Soviet Union in 1991. turkey and tajikistan accounted for three quarters (74%) of cases reported from the european region in 2006, but the case incidence in these two countries has fallen substantially since the 1990s (Fig. 5.2d).

as the total number of malaria cases diminished between 2001 and 2006, the slide positivity rate was falling in all countries except Uzbekistan, as was the fraction of cases due to P. falciparum. transmission of P. falciparum is now confined to tajikistan, mainly in the region of Khatlon. P. falciparum in countries other than tajikistan is imported.

the reduction in cases in the european region could be

attributable to indoor residual spraying (irS), the domi-nant method of vector control (chapter 4), combined with prompt treatment. However, there are apparently no direct analyses, country by country, of the impact of irS and case management, which show precisely why malaria cases were falling over the 10 years to 2006.

in the South-east asia (Fig. 5.2e, Fig. 5.3) and Western pacific regions (Fig. 5.2f), recent reductions in cases and deaths have been associated with the targeted use of itNs in Cambodia (9), india (6), lao people’s democratic repub-lic (10) and Viet Nam (11), and through prompt diagnosis and effective treatment as available, for example, at local malaria clinics in thailand.2

Malaria eliminationthe WHo Global Malaria programme aims not only to reduce the burden of malaria in endemic areas, but also to limit the geographical extent of malaria in the world. to achieve the second of these aims requires local elimination – the complete interruption of mosquito-borne malaria trans-mission in a defined geographical area (12).

WHo has identified four programmatic phases on the way to achieving and maintaining elimination: control, pre-elimination, elimination, and the prevention of reintroduc-tion (Fig. 5.4) (13). Countries make the transition from control to the pre-elimination phase when less than 5% of all suspected malaria cases have a laboratory confirmation of malaria. the elimination phase begins when there is less than 1 malaria case per 1000 people at risk per year. elimination has been achieved when the “prevention of reintroduction”, without local transmission by mosquitoes, has been successful for three or more consecutive years. While the sequence of events leading to elimination is log-ically clear, there is no evidence yet to show that malaria elimination can be achieved and maintained in areas that currently have high transmission.

By July 2008, 10 countries worldwide were in the elimi-

Fig. 5.4 Steps from malaria control to elimination

SPR: slide or rapid diagnostic test positivity rate.

CONTROL PRE-ELIMINATION ELIMINATION PREVENTION OFRE-INTRODUCTION

SPR < 5%in fever cases

< 1 case/1000 populationat risk/year

0 locallyacquired cases

3 years

WHO certification

1st programmereorientation

2nd programmereorientation

1 www.emro.who.int/rbm/index.htm.2 www.searo.who.int/eN/Section10/Section21.htm.3 azerbaijan, democratic people’s republic of Korea, Georgia, islamic

republic of iran, Kyrgyzstan, Malaysia, Mexico, Sri lanka, tajikistan, turkey and Uzbekistan.

4 China, indonesia, philippines, Solomon islands, Sudan, Vanuatu and Yemen.


nation phase: Algeria, Argentina, Armenia, Egypt, El Sal-vador, Iraq, Paraguay, Republic of Korea, Saudi Arabia and Turkmenistan. A further 11 countries were in the pre-elimination phase,3 and seven were attempting to estab-lish malaria-free zones in parts of each country.4 In Janu-ary 2007, the United Arab Emirates was the first formerly endemic country since the 1980s to be certified malaria-free by WHO. The WHO European and Eastern Mediterra-nean regions have adopted malaria elimination as (part of) their regional strategy, following the successes in several Member States.

CommentThe most significant effects of malaria control in the Afri-can Region are detectable in countries, or parts of coun-tries, with relatively small populations and high interven-tion coverage. These are Eritrea, Rwanda, Sao Tome and Principe, and Zanzibar (United Republic of Tanzania). These countries have used prevention and cure in rapid sequence or in combination, and the effects of different interventions are not easily separable. However, all 4 coun-tries and areas appear to have cut malaria burden by 50% or more between 2000 and 2006–2007, and appear to be on course to meet WHA targets by 2010. Nationwide effects of malaria control, as judged from surveillance data, are less clear in larger countries such as Madagascar and Zambia.

In other African countries that have achieved high or early intervention coverage – Burundi (use of ACT), Ethio-pia, Gambia, Kenya, Mali, Niger and Togo (widespread use of ITNs) – the expected effects on morbidity and mortality are not yet visible in routine data. The reported declines in case numbers in South Africa and Swaziland, and perhaps Namibia since 2004 (not shown in Figs 5.2 & 5.3), build on earlier successes achieved with indoor residual spraying (chapter 4) (14–16).

In many countries outside Africa, including the 25 shown in Figs 5.2 & 5.3, the numbers of cases and deaths reported to WHO were falling over the period 1997–2006. In some of these data, the causal links between interven-tions and trends are plausible, but not unequivocal, and more careful investigations of the effects of control are needed in most countries. Nevertheless, in 22 of the 25 countries, malaria cases fell by 50% or more between 2000 and 2006–2007, in line with WHA targets.

Such investigations will need to overcome the general difficulties of using surveillance data to evaluate impact, plus the problems that are specific to any region or coun-try. Inpatient records have the advantage (over outpa-tient data) that a higher proportion of cases and deaths are confirmed as malaria. However in all regions except Europe, less than half of all malaria patients present to public health facilities. So downward trends in the number of inpatients, and deaths among inpatients, as recorded by NMCPs, may not represent trends in the population at large. In the African Region, a relatively small proportion of suspected malaria cases has a confirmed diagnosis, and real trends in malaria incidence may be obscured by chang-

es in fever episodes which have a diverse set of causes. This problem will be relieved if rapid diagnostic tests (RDT) become widely available, so that confirmation of malaria prior to treatment becomes the norm (chapter 4).

In addition to their utility in evaluation, routine surveil-lance data provide continuous information for programme management – at national and district levels, and within health facilities. By compiling case and death records more carefully, and by analysing the results more thoroughly, surveillance will more effectively serve both purposes.

References1. Miller JM et al. Community-level intervention coverage and

the burden of malaria in Zambia: results of a national malaria indicator survey. (Submitted).

2. Korenromp EL et al. Measurement of trends in childhood malaria mortality in Africa: an assessment of progress toward targets based on verbal autopsy. Lancet Infectious Diseases, 2003, 3:349–358.

3. Rowe AK et al. Viewpoint: evaluating the impact of malaria control efforts on mortality in sub-Saharan Africa. Tropical Medicine & International Health, 2007, 12(12):1524–1539.

4. Nyarango PM et al. A steep decline of malaria morbidity and mortality trends in Eritrea between 2000 and 2004: the effect of combination of control methods. Malaria Journal,2006, 5:33.

5. Graves PM et al. Effectiveness of malaria control during changing climate conditions in Eritrea, 1998-2003. Tropical Medicine & International Health, 2008, 13:218–228.

6. Barat LM. Four malaria success stories: how malaria bur-den was successfully reduced in Brazil, Eritrea, India, and Vietnam. American Journal of Tropical Medicine and Hygiene,2006, 74:12–16.

7. Steketee RW et al. National malaria control and scaling up for impact: the Zambia experience through 2006. American Journal of Tropical Medicine and Hygiene, 2008, 79:45–52.

8. Bhattarai A et al. Impact of artemisinin-based combination therapy and insecticide-treated nets on malaria burden in Zanzibar. PLoS Medicine, 2007, 4:e309.

9. Chatterjee P. Cambodia’s fight against malaria. Lancet, 2005, 366(9481):191–192.

10. Kobayashi J et al. The effectiveness of impregnated bednets in malaria control in Laos. Acta Tropica, 2004, 89:299–308.

11. Hung le Q et al. Control of malaria: a successful experience from Viet Nam. Bulletin of the World Health Organization,2002, 80:660–666.

12. Global malaria control and elimination: report of a technical review. Geneva, World Health Organization, 2008.

13. Malaria elimination. A field manual for low and moderate endemic countries. Geneva, World Health Organization, 2007.

14. Mabaso ML et al. Historical review of malaria control in southern Africa with emphasis on the use of indoor residual house-spraying. Tropical Medicine & International Health,2004, 9:846–856.

15. Blumberg L, Frean J. Malaria control in South Africa – chal-lenges and successes. South African Medical Journal, 2007, 97:1193–1197.

16. Implementation of indoor residual spraying of insecticides for malaria control in the WHO African Region. Brazzaville, WHO Regional Office for Africa, 2007.

PROFILES

30 high-burden countries


…Country profiles:

methods and definitions

I. Epidemiological profilePopulation. The total population for each country is taken from the World population prospects, 2006 revision (1). The population size of children under 5 years of age is also given since this age group is particularly susceptible to malaria infection and disease.

Population by malaria endemicity. The country population is subdivided among three levels of malaria endemicity as reported by the national malaria control programme (NMCP):

1. Areas of high transmission, where the reported malaria case incidence from all species is 1 or more per 1000 population per year in 2006.

2. Areas of low transmission, where the reported malaria case incidence from all species is less than 1 per 1000 population per year in 2006, but greater than zero. Transmission in these areas is generally highly seasonal with or without epidemic peaks.

3. Malaria-free areas, where there is no continuing, local mosquito-borne malaria transmission, and all malaria cases are introduced (2). An area is designated malar-ia-free when no cases have occurred for several years. Areas may be malaria-free due to environmental factors or as a result of effective control efforts. In practice, malaria-free areas can only be accurately designated by national programmes taking into account the local epi-demiological situation and entomological and biomar-ker investigations.

If an NMCP did not provide the number of people living in high- and low-risk areas, the numbers were inferred from subnational case incidence data provided by the pro-gramme.

Population at risk. The total population living in areas where malaria is endemic (low and high transmission). The popu-lation living in malaria-free areas is excluded. The popula-tion at risk is used as the denominator when calculating the operational coverage of ITN and IRS, and hence to assess current and future needs, taking into account the population already covered.

Stratification of burden. Epidemiological maps for each coun-try are based on the malaria cases reported in 2006 at the first administrative level (province, region, state, etc.).

Four levels of endemicity are depicted:

1. 100 or more cases per 1000 population per year;

2. 1 or more cases per 1000 population per year, and less than 100 cases;

3. less than 1 case per 1000 population per year but more than zero;

4. zero recorded cases.

The first two categories correspond to the high-transmission category described above. It should be noted that case inci-dence rates for 2006 do not necessarily reflect the ende-micity of areas in previous years. If subnational data on population or malaria cases were lacking, an administrative unit was given a label of “no data” on the map. In some cases, the subnational data provided by a malaria control programme did not correspond to a mapping area known to WHO. This may be the result of modifications to administra-tive boundaries or the use of names not verifiable by WHO.

Vector and parasite profile. The species of mosquito responsi-ble for malaria transmission in a county, and the species of Plasmodium involved, are listed according to information provided by WHO regional offices.

Estimated burden of malaria. Estimates of the number of malaria cases and deaths for 2006 are given, together with lower and upper limits. The estimates for numbers of cases are for confirmed cases, i.e. fever with parasites. Each esti-mate was derived by one of two methods:

1. by adjusting the malaria cases reported by countries for reporting completeness, health-facility utilization and case-confirmation rates; or

2. if the quality of case reporting was not considered suf-ficiently high, from an empirical relationship between measures of malaria transmission risk and case inci-dence.

Estimates of the numbers of deaths were derived from an empirical relationship between measures of malaria trans-mission risk and malaria mortality rates as produced for the Global burden of disease 2004, and extrapolated to 2006. Further details of the methods are given in Annex 1.

Reported malaria cases, deaths and admissions per 1000. Report-ed malaria cases are the sum of confirmed cases (confirmed by slide examination or RDT) and probable and unconfirmed


cases (cases that were not tested but treated as malaria). NMCPs often collect data on the number of suspected cases, those tested, and those confirmed. Probable or unconfirmed cases are calculated by subtracting the number tested from the number suspected (Annex 1). Reported malaria deaths include all deaths in health facilities that are attributed to malaria, whether or not confirmed by microscopy or by RDT. Reported malaria admissions include all malaria cases admitted to a health facility with a primary diagnosis of malaria, whether or not they are confirmed by microscopy. Malaria admissions can often be taken as a proxy for severe malaria, although it is acknowledged that in some coun-tries uncomplicated P. falciparum or P. vivax cases may also be admitted.

Also shown are the national numbers of outpatient con-sultations, inpatient admissions and health-facility deaths from all causes. The numbers of all-cause consultations and deaths are useful in:

1. calculating the proportion of all outpatient and inpa-tient attendances attributed to malaria, or the extent to which a health system’s resources are consumed by malaria; and

2. assessing whether or not trends in reported malaria cases, admissions and deaths are due to differences in reporting, or to general utilization of health facilities over time.

It may also be useful to examine the ratios between reported malaria cases, admissions and deaths to deter-mine the proportion of cases admitted and the proportion of deaths. Such an analysis may provide an insight into the availability, accessibility and quality of care. However, in many situations the reporting systems for outpatient attendances are different to those for admissions and deaths (covering different types of health facility or with different reporting completeness fractions), which makes such comparisons difficult.

The graphs in the profiles show the number of reported malaria cases, admissions and deaths per 1000 popula-tion per year. The graph of reported case incidence also shows the estimated number of malaria cases per 1000 population per year, if the estimate was derived from the reported cases (after adjustments were made for reporting completeness, health service utilization and the extent of case confirmation, Annex 1). If the estimate was made from a relationship between malaria transmission risk and case incidence, estimates for years other than 2006 were not made since the method does not lend itself well to assessing change in the malaria burden over time.

If an NMCP provided an age breakdown of cases of less than and greater than or equal to 5 years of age, this is also shown. In areas of high transmission, a large fraction of cases, admissions and deaths occur in children under 5 years as compared to older ages. In areas of low trans-mission, the risks of malaria infection, disease and death are more uniform across age groups, which therefore show similar case rates per capita.

Slide examination, case confirmation, Plasmodium spp. The table shows the reported number of slides examined, the number positive and the number with a P. falciparum infection (including mixed P. falciparum and P. vivax). The graph shows four indicators:

1. Percentage of cases microscopically examined: this is the number of cases examined by microscope for every 100 suspected malaria cases. It indicates the extent to which a programme is able to provide diagnostic serv-ices to patients attending health facilities.

2. Percentage of cases confirmed: this is the number of confirmed malaria cases per 100 reported (probable and confirmed) malaria cases. This indicates the extent to which a country programme depends on the confirma-tion of malaria cases for diagnosis, treatment and epi-demiological assessment.

3. Slide positivity rate (SPR): this is the number of para-sitologically positive cases per 100 cases examined (by RDT or microscopically). SPR measures the prevalence of malaria parasites among those that seek care and are examined in health facilities.

4. Percentage of cases with P. falciparum infection: this is the number of P. falciparum cases per 100 microscopically-confirmed malaria cases.

II. Intervention policies and targetsThis section of the profile shows the policies and strategies adopted by each country with regard to malaria preven-tion, diagnosis and treatment. Policies may vary according to: (1) epidemiological setting; (2) socioeconomic factors; and (3) the capacity of a national malaria programme and/or country health system. Adoption of policies does not necessarily imply immediate implementation. Nor does it indicate full and continuous implementation nationwide. Policies and strategies are divided into those recommend-ed by WHO and those that are optional. WHO-recommended policies and strategies include (see also chapter 2):

1. provision of free or highly-subsidized LLINs to all age groups at risk of malaria (3);

2. use of IRS, including DDT (4);

3. use of IPT in highly-endemic countries with compara-tively low levels of resistance to SP (5);

4. parasitological confirmation for all age groups;

5. banning of oral artemisinin monotherapies;

6. provision of artemisinin-based combination therapy (ACT) for malaria cases infected with P. falciparum free or highly subsidized in the public sector (6).1

Optional policies or strategies are policies adopted by coun-tries taking local epidemiological and other circumstances

1 Currently tolerability data are insufficient to recommend it for use in African children.


into account. “Yes” implies that the policy or strategy is adopted regardless of the scale of implementation; ”no” implies that the policy is not adopted; and “not applicable” implies that the policy is irrelevant to the context of the country situation. The year of adoption of a policy shows the year in which the policy or strategy was approved by an NMCP, but does not take into account any change that may have occurred after the reports were received.

III. Implementing malaria controlCoverage of ITNs, from survey data. The percentage of house-holds that own at least one mosquito net, and the percent-age of children under 5 years who slept under a net, are taken from nationally representative household surveys such as the MICS, DHS and MIS. The results of subnational surveys undertaken to support local project implementa-tion are difficult to interpret nationwide, and hence are not presented in the profiles, although they can be very useful in assessing progress locally. It should be noted that most MICS and DHS surveys are conducted during the dry season for logistic reasons, and estimates may not reflect usage during peak malaria transmission (during which ITNuse may be higher).

Coverage of IRS and ITNs, from programme data. Because many countries do not have recent national survey data, NMCPdata on the number of mosquito nets distributed and hous-es sprayed were used to estimate the operational coverage of ITNs and IRS. ITN operational coverage is calculated as the number of ITNs distributed, divided by the population at risk (sum of population living in low- and high-transmis-sion areas) divided by two (a ratio of one ITN for every two persons, following WHO recommendations) and multiplied by 100 (4). On average, LLINs distributed are considered to have a useful lifespan of three years, hence the cumula-tive total of mosquito nets distributed over the past three years is taken as the numerator for any particular year. Other ITN are considered to have an average lifespan of one year; some nets will be effective over a longer period if retreated with insecticide. Retreatment is not taken into account in this report, and is in any case becoming less frequent with the advent of LLIN. It should be noted that such operational estimates contain no information about the geographical distribution of ITNs, or their distribution within households. ITNs may be clustered in certain sub-populations, thus depriving others at risk, and the number of ITNs delivered to a household may exceed or fall short of the recommended ratio of one net per two people.

The operational coverage of IRS is calculated as the number of people living in a household where IRS has been conducted during the preceding 12 months, divided by the population at risk (the sum of populations living in low- and high-transmission areas) multiplied by 100. Respond-ents were asked to convert, where necessary, records of the number of built structures sprayed to the number of households, where the average household consists of more than one structure. The number of people protected by

IRS was determined by multiplying the number of house-holds sprayed by 5 (the average household size was found to be 4.9 in an analysis of the latest DHS data from 52 malaria-endemic countries across the world; and 5.0 in 24 countries in the WHO African Region). Programme data are the most important source of information for estimating IRS coverage, as household surveys have not generally included questions on IRS. In addition, IRS is often carried out on a limited geographical scale over which nationally representative household surveys may not provide an ade-quate sample size for coverage to be measured accurately. The percentage of people protected by IRS measures the extent to which IRS is implemented nationwide. It should be noted that the data show neither the quality of spray-ing nor the geographical distribution of IRS coverage in a country, which is typically focal.

Access by febrile children to effective treatment, from survey data. Estimates of the percentage of children under 5 years with fever that were treated with antimalarial medicines, together with the type of antimalarial medicine, were obtained from nationally representative household surveys such as MICS, DHS and MIS. These estimates should be interpreted with the following provisos:

1. not all fever cases are malaria, particularly in low-transmission areas, so one may not expect 100% of febrile children to receive an antimalarial, particularly if they seek formal health care and laboratory diagnosis excludes malaria;

2. most DHS and MICS surveys are conducted during the dry season and data may not reflect the year-round inci-dence of malarial disease, and the provision of antima-larial treatment during the period of peak incidence;

3. it may be difficult to exclude some non-malarious areas from the analysis, and rates of antimalarial treatment compared to estimated need may appear unduly low;

4. respondents in household surveys may not recall accu-rately the type of medicine provided to children; the graph in the profile shows the use of any antimalarial and use of ACT. Access to ACT may also appear unduly low in countries where chloroquine is used to treat P. vivax,especially where P. vivax causes a high proportion of malaria cases. As ACT was introduced comparatively recently, surveys commonly report only on the use of any antimalarial.

Access to effective treatment, from programme data. This is esti-mated as the number of ACT treatment courses delivered by a national malaria control programme per 100 cases requir-ing treatment in a year. The number requiring treatment in a year depends not only on the incidence of malaria but also on the rate of case confirmation. In countries that con-firm all cases, the number requiring treatment will equal the number of confirmed cases. In countries that do not undertake case confirmation, it will be equal to the number of fever cases suspected of being malaria. In general:


T = F x e x s + (F x (1–e))= MC + (F x (1–e))

where:

T = the number of treatment courses required per yearF = the estimated fever cases suspected of being malaria

per yeare = the proportion of suspected cases testeds = the slide positivity rateMC = the estimated confirmed malaria cases per year.

The graphical presentation in the country profiles makes the simple assumption that all fever cases (F) need treat-ment in every region of the world except Europe, where all cases are assumed to be confirmed. This is therefore a crude assessment (typically an underestimate) of the extent to which an NMCP has made effective antimalar-ial medicines available to those who need treatment in any given year. It should also be noted that the need is estimated across the public and private sectors and for cases to be treated in the community, whereas a national malaria control programme may focus only on supplying health facilities in the public sector. The number of ACTtreatment courses delivered by the NMCP may not reflect the number of cases actually treated with ACT, particularly if medicines are held in store.

IV. Financing malaria controlGovernment and external financing. NMCP budgets and expendi-tures may be used to assess the extent to which national malaria programmes are able to maintain or scale up access to malaria prevention, diagnosis and treatment. The data shown are those reported by the national malaria control programme. The first graph shows financial contributions by source or name of agency by year. The government con-tribution is normally the declared government expenditure for the year. If government expenditure was not report-ed by a programme, the government budget was used.1

External contributions are contributions allocated to the programme by external agencies that may or may not be disbursed. Additional information about the contributions from specific donors, as reported by the donors themselves, is given in Annex 7.

Breakdown of expenditure by intervention. The graph shows how malaria funding was spent on different activities: ITNs, IRS, diagnosis, treatment, and other programme-related expenses. Figures do not distinguish expenditures on commodities, human resources, transport and other costs involved in specific activities. All countries were requested to convert local currencies to 2006 US$. Quantities have not been adjusted for purchasing power parity. If annual plans are completed as anticipated, the amounts shown should be about the same as the total amount received by the programme. However, some divergence may occur through unexpectedly slow or fast disbursement of donor contributions or implementation, or through changes in plans, prices and other factors. There may also be differ-ences in the completeness of data, and expenditures on the activities listed may not include all items of expendi-ture. Despite the various uncertainties associated with these data, the graphs are able to highlight major changes in programme funding and expenditure.

V. Sources of information Sources of data are shown at the end of each profile. The WHO Global Malaria Programme has created an Access database containing the information used in compiling this report. The data, together with profiles for all 109 malaria endemic countries, are available from www.who.int/topics/malaria/en/.

References1. United Nations Population Division. World population pros-

pects. New York, United Nations, 2006.

2. Malaria elimination: a field manual for low and moderate endemic countries. Geneva, World Health Organization, 2007.

3. Global Malaria Programme. WHO position statement on ITNs.Geneva, World Health Organization, 2007.

4. Global Malaria Programme. Use of indoor residual spraying for scaling up global malaria control and elimination. Geneva, World Health Organization, 2006 (WHO/HTM/MAL/2006.1112).

5. A strategic framework for malaria prevention and control dur-ing pregnancy in the African Region. Brazzaville WHO Region-al Office for Africa, 2004 (AFRO/MAL/04/01).

6. Global Malaria Programme. Guidelines for the treatment of malaria. Geneva, World Health Organization, 2006 (WHO/HTM/MAL/2006.1108).

1 The government budget is not the amount required for full imple-mentation of malaria control, but represents the amount of domestic resources allocated by a government’s treasury.


Atlantic Ocean

Democratic Republic of the Congo

Zambia

Angola

I. EPIDEMIOLOGICAL PROFILE

Population, endemicity and malaria burden

Population (000) 2006 %

All age groups 16 557< 5 years 3 082 19

5 years 13 475 81

Population by malaria endemicity (000) 2006 %

High transmission 1/1000 16 557 100Low transmission (0–1/1000) 0 0Malaria-free (0 cases) 0 0Rural population 7 620 46

Vector and parasite profile

Major Anopheles species: arabiensis, coustani, flavicosta, funestus, gambiae, melas, nili,paludis, pharoensis

Plasmodium species: falciparum, vivax

Stratification of burden (reported cases/1000)

Estimated burden of malaria Estimated cases and deaths (2006) Estimated cases and deaths per 1000 (2006)

Age group Numbers Lower Upper Per 1000 Lower Upper

Fever suspected of being malaria All ages 10 062 000 7 007 000 13 223 000 608 423 799< 5 years 5 300 000 1 254 000 9 833 000 1 719 407 3 190

Malaria cases All ages 3 555 000 2 475 000 4 672 000 215 149 282< 5 years 1 872 000 443 000 3 474 000 607 144 1 127

Malaria deaths All ages 21 000 12 000 32 000 1.3 0.73 1.9< 5 years 17 000 9 700 26 000 5.5 3.1 8.4

Malaria case-fatality rate (%) All ages 0.59 — — —< 5 years 0.91 — — —

Trends in malaria morbidity and mortality

Reported malaria cases, per 1000 Reported malaria deaths, per 1000

Reported malaria 1 249 767 1 862 662 3 246 258 2 489 170 2 329 316 2 283 097 2 726 530cases, all ages

Reported malaria 815 314 770 639 1 097 783cases, < 5 years

All-cause outpatient 1 971 655 2 919 857 4 293 505 3 829 317 3 608 468 3 833 556consultations, all ages

All-cause outpatientconsultations, < 5 years

Reported malaria 9 473 14 434 38 598 12 459 13 768 10 220 9 812deaths, all ages

Reported malaria 7 354 5 634 5 452deaths, < 5 years

All-cause deaths, 15 206 24 503 46 406 19 419 20 896 20 646 16 560all ages

All-cause deaths, 10 720< 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

100

200

300

400

2001 2002 2003 2004 2005 2006 2007

Reported malaria cases/1000, all agesReported malaria cases/1000, < 5

Mala

ria d

eath

s per

100

0

0

1

2

3

4

5

2001 2002 2003 2004 2005 2006 2007

Reported malaria deaths/1000, all agesReported malaria deaths/1000, < 5

Angola had an estimated 3.5 million malaria cases in 2006. Transmission occurs all year round, but is seasonal in the south. Less than half of all sus-pected cases are confirmed as malaria. Case and death reports were variable between 2001 and 2006 and there was no evidence of any systematicdecline. Implementation of IRS, which began in 2003, is not consistent over the years. The NMCP distributed about 2.5 million LLINs in 2006 and 2007,adequate to cover only 30% of the 16 million people at risk. In the 2006–2007 survey, 33% of households had a mosquito net, but only 18% of childrenslept under an ITN. The programme delivered 1.7 million ACT courses in 2006 and 2.03 million in 2007. Funding increased from US$ 16 million in 2004to over US$ 46 million in 2007, financed by the government, Global Fund, UN agencies, World Bank, bilateral agencies and others.


Reported malaria admissions, per 1000 Slide examination, case confirmation, Plasmodium spp

Examined

Positive 889 572 1 029 198 1 295 535

P. falciparum

II. INTERVENTION POLICIES AND TARGETSIntervention WHO-recommended policies/strategies Optional policies/strategies

Policy/strategy Yes/ Year Policy/strategy Yes/ YearNo adopted No adopted

Insecticide-treated nets (ITN) Distribution of LLINS – Free Yes 2001 Distribution – Antenatal care Yes 2001

Targeting – All age groups No — Distribution – EPI routine and campaign Yes 2005

Targeting – Children under 5 years and Yes 2000pregnant women

Indoor residual spraying (IRS) DDT is used for IRS (public health) only No — DDT is used alternately with other No —insecticides in the same area

Insecticide-resistance management Yes 2005 IRS is the primary vector-control intervention Yes 2003implemented

IRS is used for prevention and control Yes 2003of epidemics

Where IRS is conducted, other options Yes 2003are also implemented, e.g. ITN

Intermittent preventive IPT used to prevent malaria during Yes 2005 IPT implemented countrywide Yes 2005treatment (IPT) pregnancy

Case management ACT is free or highly subsidized in public Yes 2005 Free malaria diagnosis and first-line Yes —sectors treatment of malaria

Oral artemisinin monotherapies banned Yes 2004 Home management of malaria No —

Parasitological confirmation for all age groups Yes 2001 Prereferral treatment at health-facility level No —with quinine im or artesunate suppositories

RDTs in areas without microscopy Yes 2005

Antimalarial medicines Type of Yearmedicine adopted

First-line treatment of P. falciparum AL 2006(unconfirmed)

First-line treatment of P. falciparum AL 2006(confirmed)

Treatment failure of P. falciparum QN(7d) 2006

Treatment of severe malaria QN(7d) 2006

Treatment of P. vivax — —

I. EPIDEMIOLOGICAL PROFILE (continued)

ANG

OLA

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

% of cases microscopically examined% of cases confirmedSlide-positivity rate (SPR)% of cases with P. falciparum infection

Reported malaria 124 977 186 266 324 626 248 917 232 932 228 310 272 653admissions (all ages)

Reported malaria 36 307 72 102 112 913 91 039 81 531 77 064 109 778admissions (< 5)

All-cause admissions,all ages

All-cause admissions,< 5 years

Adm

itted

case

s per

100

0

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

Reported malaria admissions/1000, all agesReported malaria admissions/1000, < 5


III. IMPLEMENTING MALARIA CONTROLCoverage of ITN: survey data Coverage of IRS and ITN: programme data

V. SOURCES OF INFORMATIONProgramme data Survey and other data

Reported cases Min. Santé Surveillance data Insecticide-treated nets (ITN) MIS 2006–07

Operational coverage of ITNs, IRS and access to medicines OMS, GF, PMI Programme report Treatment MIS 2006–07

Financial data Min. Santé Programme report Use of health services Imputed

Access by febrile children to effective treatment: survey data Access to effective treatment: programme data

IV. FINANCING MALARIA CONTROLGovernmental and external financing Breakdown of expenditure by intervention

ANG

OLA

% of pregnant women 25who slept under any net

% of pregnant women 22who slept under an ITN

Source: MIS 2006–2007.

No. of households 118 000 132 436protected by IRS

No. of ITNs 204 600 450 000 430 000 45 889 1 051 286 1 495 165and/or LLINs

No. of first-line 1 700 000 2 031 760treatmentcourses received

No. of ACT treatment 1 736 200 2 031 760courses received

Others 3 500 000 2 200 000 8 000 000

Bilaterals 2 100 000 9 000 000 21 500 000

European Union 0 0

GFATM 15 107 895 2 354 259 11 011 200

World Bank 0 330 000 500 000

UN agencies 1 062 266 1 540 225 1 243 000

Gov. malaria 3 449 000 1 169 000 18 024 239 16 135 633 13 509 356 632 722 3 482 407expenditure

% of children 25< 5 years with fever

% of febrile children 62< 5 years who soughttreatment in health facility

Source: MIS 2006–2007.

No data

% of households with any net% of households with at least one ITN% of < 5 years who slept under any net% of < 5 years who slept under an ITN

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007

Operational IRS coverage (relative to total population at risk)Operational coverage of ITN (1 LLIN or ITN per 2 persons at risk)Operational coverage of any net (per 2 persons at risk)

Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with fever who took antimalarial drugs% of children < 5 years with fever who took antimalarial drugssame or next day% of children < 5 years with fever who took ACT% of children < 5 years with fever who took ACT same or next day

Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Operational coverage of first-line treatment (relativeto estimated fever cases in need of treatment)Operational coverage of ACT (relative to estimatedfever cases in need of treatment)

Fund

ing b

y sou

rce (

$m)

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007

42 World Malaria report 2008

India

Bay of Bengal

Myanmar

BangladeshBangladesh had an estimated 2.9 million malaria cases and 15 000 deaths in 2006. Although 72% of the population are at some risk of malaria, the risk is greatest in the east and north-east of the country in areas bordering India and Myanmar. The majority of suspected cases are unconfirmed; among those that are identified as malaria, more than 70% are P. falciparum. With large annual fluctuations in reported cases, there is no evidence of a systematic decline in malaria between 2001 and 2006. No malaria deaths were reported over this period. IRS has been used selectively; ITN coverage is also low. ACT was adopted as treatment policy in 2004, but the number of courses of treatment available in 2005 and 2006 were far fewer than the number of reported cases. Malaria control is financed mainly by government, the World Bank and the Global Fund, exceeding US$ 20 million in 2006.

I. EpIdEmIologIcal profIlE



All age groups 155 991 < 5 years 18 951 12≥ 5 years 137 040 88


High transmission ≥ 1/1000 17 868 11Low transmission (0–1/1000) 95 403 61Malaria-free (0 cases) 42 720 27Rural population 116 227 75


Major Anopheles species: dirus, minimus, philippinensis, sundaicus Plasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 18 910 000 6 341 000 44 962 000 121 41 288 < 5 years 749 000 251 000 1 782 000 40 13 94

Malaria cases All ages 2 975 000 1 108 000 6 677 000 19 7.0 43 < 5 years 118 000 44 000 265 000 6.0 2.0 14

Malaria deaths All ages 6 600 2 200 12 000 0.04 0.01 0.78 < 5 years 3 900 1 300 7 000 0.21 0.07 0.37

Malaria case-fatality rate (%) All ages 0.22 — — — < 5 years 3.3 — — —


Reported and estimated malaria cases, per 1000 Reported malaria deaths, per 1000

Reported malaria 361 586 307 236 491 602 215 678 190 724 362 042 cases, all ages

Reported malaria 8 967 7 186 cases, < 5 years

All-cause outpatient consultations, all ages

All-cause outpatient consultations, < 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

Reported malaria/1000, all agesReported malaria/1000, <5Estimated malaria/1000, all agesEstimated malaria/1000, <5

Malar

ia de

aths p

er 10

00

0

0.001

0.002

0.003

0.004

0.005

2001 2002 2003 2004 2005 2006 2007


Reported malaria 470 598 574 411 481 506 deaths, all ages

Reported malaria deaths, < 5 years

All-cause deaths, all ages

All-cause deaths, < 5 years



Examined

Positive 55 646 63 516 56 879 59 853 48 096 48 248

P. falciparum 39 555 46 363 42 012 46 637 37 754 35 487



Insecticide-treated nets (ITN) Distribution of LLINS – Free Yes — Distribution – Antenatal care No —

Targeting – All age groups Yes — Distribution – EPI routine and campaign No —

Targeting – Children under 5 years and Yes —pregnant women


Insecticide-resistance management No — IRS is the primary vector-control intervention No —implemented

IRS is used for prevention and control Yes —of epidemics

Where IRS is conducted, other options Yes —are also implemented, e.g. ITN

Intermittent preventive IPT used to prevent malaria during No — IPT implemented countrywide No —treatment (IPT) pregnancy

Case management ACT is free or highly subsidized in public Yes — Free malaria diagnosis and first-line Yes —sectors treatment of malaria

Oral artemisinin monotherapies banned Yes — Home management of malaria No —

Parasitological confirmation for all age groups Yes — Prereferral treatment at health-facility level No —with quinine im or artesunate suppositories

RDTs in areas without microscopy Yes —


First-line treatment of P. falciparum CQ+PQ 2004(unconfirmed)


Treatment failure of P. falciparum QN+T or d 2004

Treatment of severe malaria QN/AM 2004

Treatment of P. vivax CQ+PQ(14d) —


BANG

LADE

SH

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


No data




Reported cases Surveillance data Insecticide-treated nets (ITN) No surveys

Operational coverage of ITNs, IRS and access to medicines Programme report Treatment No surveys

Financial data Programme report Use of health services DHS 2004



BANG

LADE

SH

No. of householdsprotected by IRS

No. of ITNs 129 660 168 500 305 000and/or LLINs

Others

Bilaterals

European Union

GFATM 4 107 720

World Bank 84 000 292 000 17 445 000

UN agencies 185 000 185 000 225 000

Gov. malaria 65 000 68 000 360 000expenditure



Source: MICS 2006.

No. of first-linetreatmentcourses received

No. of ACT treatment 37 754 35 448courses received

No data Operational IRS coverage (relative to total population at risk)Operational coverage of ITN (1 LLIN or ITN per 2 persons at risk)Operational coverage of any net (per 2 persons at risk)

Perc

enta

ge

0

0.5

1

1.5

2

2001 2002 2003 2004 2005 2006 2007

No data

Perc

enta

ge

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

5

10

15

20

25

2001 2002 2003 2004 2005 2006 2007

No data

2001 2002 2003 2004 2005 2006 2007


Bolivia

Argentina

PeruAtlantic Ocean

Pacific Ocean

BrazilBrazil had an estimated 1.4 million malaria cases in 2006, over half of the total for the WHO Region of the Americas. More than 350 000 cases werereported annually over the period 2001–2007, with a maximum exceeding 600 000 in 2005. Transmission occurs mainly in the Legal Amazon Regionwhere 10–15% of the population is at risk. Almost all reported malaria cases are confirmed, and 19% were P. falciparum in 2007. IRS is the principalmethod of mosquito control, but is applied sporadically. The last round of IRS was in 2005. There is no national policy on the use of ITNs. First-lineantimalarial drugs are apparently sufficient to treat all reported cases, and ACT was introduced in 2007. Funding for malaria control increased to morethan US$ 100 million in 2006, provided exclusively by the government.





5 years 171 231 90


High transmission 1/1000 23 928 13Low transmission (0–1/1000) 0 0Malaria-free (0 cases) 165 395 87Rural population 28 883 15


Major Anopheles species: albitarsis, albimanus, darlingi, nuneztovariPlasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 11 195 000 6 874 000 15 833 000 59 36 84< 5 years — —

Malaria cases All ages 1 379 000 1 018 000 1 770 000 7.0 5.0 9.0< 5 years — —

Malaria deaths All ages 1 000 630 1 500 0.01 0.00 0.01< 5 years 280 170 410 0.02 0.01 0.02

Malaria case-fatality rate (%) All ages 0.07 — — —< 5 years — — —



Reported malaria 388 303 348 259 408 821 464 602 603 026 549 184 458 041cases, all ages

Reported malaria 53 763cases, < 5 years

All-cause outpatient 973 195 070consultations, all ages


Reported malaria 142 93 103 100 122 97 64deaths, all ages

Reported malaria 9 2 11deaths, < 5 years

All-cause deaths, 961 492 982 807 1 002 340 1 024 073 1 006 827 1 001 419 855 014all ages


No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.0005

0.001

0.0015

0.002

2001 2002 2003 2004 2005 2006 2007





Insecticide-treated nets (ITN) Distribution of LLINS – Free Yes 2007 Distribution – Antenatal care No —

Targeting – All age groups Yes 2007 Distribution – EPI routine and campaign No —

Targeting – Children under 5 years and No —pregnant women


Insecticide-resistance management Yes 2004 IRS is the primary vector-control intervention Yes —implemented









First-line treatment of P. falciparum — 2006(unconfirmed)


Treatment failure of P. falciparum — 2006

Treatment of severe malaria AS or AM or QN 2006




Reported malaria 901admissions (< 5)

All-cause admissions, 11 337 831 11 299 863all ages

All-cause admissions, 1 356 404< 5 years

Examined 2 274 610 2 118 491 2 269 359 2 584 397 3 196 788 3 504 379 2 979 566

Positive 388 303 348 259 408 821 464 602 603 026 549 184 458 041

P. falciparum 77 549 76 337 83 765 104 287 146 815 136 837 88 249


BRA

ZIL

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Adm

itted

case

s per

100

0

0

0.02

0.04

0.06

0.08

0.1

2001 2002 2003 2004 2005 2006 2007



BRA

ZILIII. IMPLEMENTING MALARIA CONTROL

Coverage of ITN: survey data Coverage of IRS and ITN: programme data


Reported cases PAHO MCQ Surveillance data Insecticide-treated nets (ITN) No surveys

Operational coverage of ITNs, IRS and access to medicines PAHO MCQ Programme report Treatment No surveys

Financial data PAHO MCQ Programme report Use of health services DHS 1996



No. of households 400 000 1 071 340 437 417protected by IRS

No. of ITNs 10 000and/or LLINs

No. of first-line 665 253 504 563 457 136 744 099 1 223 538 458 041treatmentcourses received

No. of ACT treatment 35 382courses received

Others 125 000

Bilaterals 0

European Union 0

GFATM 0

World Bank 350 000

UN agencies 0

Gov. malaria 21 517 299 21 411 765 40 695 955 40 695 955 73 469 000 10 600 000expenditure

Not applicable Operational IRS coverage (relative to total population at risk)Operational coverage of ITN (1 LLIN or ITN per 2 persons at risk)Operational coverage of any net (per 2 persons at risk)

Perc

enta

ge

0

10

20

30

2001 2002 2003 2004 2005 2006 2007

Not applicable

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

20

40

60

80

100

120

2001 2002 2003 2004 2005 2006 2007

No data


MaliNiger

Benin

TogoGhanaCôte

d’Ivoire

Burkina Faso





5 years 11 754 82




Major Anopheles species: arabiensis, brochieri, coustani, flavicosta, funestus, gambiae,hancocki, nili, paludis, pharoensis





Fever suspected of being malaria All ages 23 015 000 12 001 000 34 724 000 1 603 836 2 418< 5 years 14 013 000 2 343 000 27 055 000 5 380 899 10 386

Malaria cases All ages 6 227 000 3 247 000 9 394 000 434 226 654< 5 years 3 791 000 634 000 7 320 000 1 455 243 2 810

Malaria deaths All ages 26 000 12 000 43 000 1.8 0.84 3.0< 5 years 25 000 12 000 42 000 9.6 4.6 16




Reported malaria 352 587 1 188 870 1 443 184 1 546 644 1 615 695 2 060 867 2 487 633cases, all ages

Reported malaria 124 403 546 940 671 643 701 935 770 986 977 988 1 180 926cases, < 5 years

All-cause outpatient 2 604 791 3 753 393 4 402 278 4 462 249 5 346 113 5 307 006 6 108 633consultations, all ages

All-cause outpatient 1 071 939 1 519 812 1 810 555 1 791 775 2 035 035 2 366 588 2 336 670consultations, < 5 years


Reported malaria 2 878 2 883 3 387 3 021 4 189 6 762 4 865deaths, < 5 years


All-cause deaths, 6 388 4 400 7 445 7 139 7 412 8 968 5 623< 5 years

Mala

ria ca

ses p

er 1

000

0

100

200

300

400

500

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007


No data 0 0–1 1–100 > 100

Burkina Faso has around 3% of all malaria cases in the WHO African Region. Malaria is endemic in the southern third of the country, occurring season-ally between December and April. Almost all cases are caused by P. falciparum, though most are unconfirmed despite recent improvements in diagnos-tic services. The numbers of reported cases and deaths have increased in recent years; it is not known if this reflects a real increase in malaria burdenor improved reporting. The NMP distributed approximately 800 000 ITNs in 2005 and 2006, of which 520 000 were LLINs. This is far below the numberneeded to protect 14 million people at risk. IRS is not a national policy. The NMP reported delivery of 811 507 ACT courses only in 2007, inadequate tocover all estimated cases in need of treatment. Malaria control has been funded mainly by the government, the Global Fund and the World Bank.





All-cause admissions, 218 215 627 070all ages


Examined 30 006 32 796 31 256 52 874 73 262 122 047 127 120

Positive 18 256 21 335 44 265 44 246

P. falciparum




Targeting – All age groups Yes 1998 Distribution – EPI routine and campaign Yes



Insecticide-resistance management Yes 1998 IRS is the primary vector-control intervention No —implemented

IRS is used for prevention and control No —of epidemics

Where IRS is conducted, other options No —are also implemented, e.g. ITN


Case management ACT is free or highly subsidized in public No — Free malaria diagnosis and first-line No —sectors treatment of malaria

Oral artemisinin monotherapies banned Yes 2005 Home management of malaria Yes 1998


RDTs in areas without microscopy No —


First-line treatment of P. falciparum AL —(unconfirmed)

First-line treatment of P. falciparum AL —(confirmed)

Treatment failure of P. falciparum QN(7d) —

Treatment of severe malaria QN(7d) —



Adm

itted

case

s per

100

0

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge


BUR

KIN

AFA

SO




Reported cases DEP/MS Surveillance data Insecticide-treated nets (ITN) DHS 2003, MICS 2006

Operational coverage of ITNs, IRS and access to medicines PNLP/MS Programme report Treatment DHS 2003, MICS 2006

Financial data PNLP/MS Programme report Use of health services DHS 1998



BUR

KIN

AFA

SO



Source: DHS 2003, MICS 2006.


No. of ITNs 401 500 121 100 13 000and/or LLINs

No. of first-line 3 643 062 5 621 064 6 084 223 5 191 738 4 167 908 3 930 296 4 981 270treatmentcourses received


Others 0 100 974 38 740 207 561 359 861 325 000 0

Bilaterals 0 0 0 200 000 215 247 200 000 0

European Union 0 0 0 0 0 0 0

GFATM 0 0 0 2 925 513 4 193 558 0 0

World Bank 0 0 0 0 0 12 000 000 0

UN agencies 0 241 701 177 551 1 153 484 457 411 747 985 117 037

Gov. malaria 56 393 95 868 151 567 197 387 200 000 1 119 648 1 058 476expenditure





Perc

enta

ge

WHO 2010 target

2001 2002 2003 2004 2005 2006 20070

20

40

60

80

100Operational IRS coverage (relative to total population at risk)Operational coverage of ITN (1 LLIN or ITN per 2 persons at risk)Operational coverage of any net (per 2 persons at risk)

Perc

enta

ge

0

5

10

15

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

5

10

15

2001 2002 2003 2004 2005 2006 2007

No data


Gulf of Thailand

Thailand

Viet Nam

Cambodia





5 years 12 506 88


High transmission 1/1000 6 232 44Low transmission (0–1/1000) 1 334 9Malaria-free (0 cases) 6 630 47Rural population 11 312 80


Major Anopheles species: dirus, minimus, sundaicusPlasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 820 000 587 000 1 100 000 58 41 77< 5 years 58 000 42 000 78 000 34 25 46

Malaria cases All ages 262 000 18 14 24< 5 years 19 000 14 000 24 000 11 8.0 14

Malaria deaths All ages 580 350 840 0.04 0.03 0.06< 5 years 230 140 340 0.14 0.08 0.2





Reported malaria 9 854 8 057 7 415 6 163 4 566 5 135 3 031cases, < 5 years


All-cause outpatient 689 728 747 870 988 026 1 201 908 1 466 784 3 965consultations, < 5 years


Reported malaria 84 67 77 50 49 59 25deaths, < 5 years


All-cause deaths, 625 649 622 736 765 683< 5 years

No data 0 0–1 1–100 > 100

Approximately 2 million people live in or around forested areas that have intense malaria transmission. Soldiers, forestry workers and gem miners are at highrisk; children living in forest villages are also particularly vulnerable. Cambodia had an estimated 262 000 cases and 580 deaths in 2006, but the reportednumbers of cases, hospital admissions and deaths were falling over the period 2001–2006. A 2005 survey found that most households own a mosquito netbut few are ITNs. Under national treatment policy, artesunate and mefloquine are distributed together in blister packs through public and private sectors,though resistance to these drugs has been recorded on the Cambodia-Thailand border. National policy also promotes the use of rapid diagnostic tests sothat antimalarial treatment is targeted to malaria patients. Funding for malaria control appears to have increased, especially through support from the GlobalFund in 2007.

Mala

ria ca

ses p

er 1

000

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.02

0.04

0.06

0.08

0.1

2001 2002 2003 2004 2005 2006 2007





Reported malaria 573 450 542 387 325 519 295admissions (< 5)

All-cause admissions, 283 140 250 314 305 654 314 627 287 151 388 890 204 681all ages

All-cause admissions, 83 669 61 415 94 447 96 039 77 639 97 773 41 454< 5 years

Examined 121 691 108 967 106 330 99 593 88 991 94 460 135 731

Positive 42 150 38 048 42 234 37 389 26 914 33 010 42 518

P. falciparum 48 556 41 170 63 022 52 731 40 004 69 925 16 518











Case management ACT is free or highly subsidized in public Yes 2000 Free malaria diagnosis and first-line Yes 2000sectors treatment of malaria

Oral artemisinin monotherapies banned Yes — Home management of malaria Yes 2002

Parasitological confirmation for all age groups Yes 2000 Prereferral treatment at health-facility level Yes 2000with quinine im or artesunate suppositories



First-line treatment of P. falciparum AS+MQ 2000(unconfirmed)

First-line treatment of P. falciparum AS+MQ 2000(confirmed)

Treatment failure of P. falciparum QN(7d) +T(7d) 2000

Treatment of severe malaria AM+MQ 2000

Treatment of P. vivax CQ —


CAM

BODI

AAd

mitt

ed ca

ses p

er 1

000

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

% of cases confirmedSlide-positivity rate (SPR)




Reported cases HIS Surveillance data Insecticide-treated nets (ITN) DHS 2005

Operational coverage of ITNs, IRS and access to medicines HIS Programme report Treatment DHS 2005

Financial data HIS Programme report Use of health services DHS 2005



CAM

BODI

A



Source: DHS 2005.



Others 106 927

Bilaterals

European Union 1 257 000 1 900 000 0 0 0 0

GFATM 0 0 0 537 378 1 345 572 1 901 220 5 762 926

World Bank 643 000 900 000 50 000 490 014 283 494 306 709 918 403

UN agencies 500 000 500 000 500 000 520 000 500 000 500 000 500 000

Gov. malaria 316 000 465 000 240 000 933 156 1 332 647 1 282 500 1 456 419expenditure



Source: DHS 2005.

Others

Treatment 476 747 564 101 830 379 1 581 353 745 350 1 069 913 1 475 696

Diagnosis 20 902 286 748 117 480 185 610 914 953 16 707 166 126

IRS

ITN 248 826 369 806 617 545 2 808 450 1 839 856 1 225 594 578 440

No. of first-line 61 550 169 784 127 982 89 993 77 782 141 535 91 839treatmentcourses received

No. of ACT treatment 24 430 116 184 127 382 84 421 75 082 112 495 150 819courses received


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

0.2

0.4

0.6

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with fever who took antimalarial drugs% of children < 5 years with fever who took antimalarial drugs same or next day% of children < 5 years with fever who took ACT% of children < 5 years with fever who took ACT same or next day

Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

1

2

3

4

5

2001 2002 2003 2004 2005 2006 2007


Atlantic Ocean

Chad

Congo

Central African Republic

Nigeria

Cameroon





5 years 15 324 84


High transmission 1/1000 12 889 71Low transmission (0–1/1000) 5 286 29Malaria-free (0 cases) 0 0Rural population 8 087 44


Major Anopheles species: arabiensis, brochieri, coustani, flavicosta, funestus, gambiae,hancocki, hargreavesi, melas, moucheti, nili, paludis, pharoensis





Fever suspected of being malaria All ages 14 705 000 9 668 000 19 921 000 809 532 1 096< 5 years 7 993 000 1 505 000 15 253 000 2 804 528 5 350

Malaria cases All ages 5 091 000 3 347 000 6 897 000 280 184 379< 5 years 2 767 000 521 000 5 281 000 971 183 1 852





Reported malaria 277 413 634 507cases, all ages

Reported malaria 89 041 227 284cases, < 5 years

All-cause outpatient 697 665 1 748 905consultations, all ages

All-cause outpatient 197 771 462 140consultations, < 5 years

Reported malaria 836 930deaths, all ages

Reported malaria 603 578deaths, < 5 years

All-cause deaths, 254 308 261 175all ages


No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.1

0.2

0.3

0.4

0.5

2001 2002 2003 2004 2005 2006 2007


Cameroon had an estimated 5 million malaria cases in 2006. Transmission occurs all year round but is more intense in the south. None of the 635 000cases reported in 2006 were confirmed as malaria. Cases were reported only in 2005 and 2006, and there is no information about malaria deaths overthe period 2001–2006. IRS is not national policy. The NMCP distributed over 1 million ITNs in 2006 of which only 17 000 were LLINs, far fewer thanare needed to protect the 18 million people at risk. A 2006 survey found that 32% of households owned a mosquito net and 20% an ITN, but only 13%of children slept under an ITN. 58% of children with fever received an antimalarial drug, but only 2% were given ACT. Funding for malaria control wasaround US$ 12.5 million in 2006, provided mostly by government and supported by the Global Fund.



Reported malaria 75 738 75 904admissions (all ages)

Reported malaria 23 418 27 636admissions (< 5)

All-cause admissions, 138 617 204 498all ages

All-cause admissions, 37 928 66 873< 5 years






Indoor residual spraying (IRS) DDT is used for IRS (public health) only No — DDT is used alternately with other No — insecticides in the same area


IRS is used for prevention and control No — of epidemics

Where IRS is conducted, other options No — are also implemented, e.g. ITN


Case management ACT is free or highly subsidized in public No 2007 Free malaria diagnosis and first-line No —sectors treatment of malaria


Parasitological confirmation for all age groups Yes — Prereferral treatment at health-facility level No — with quinine im or artesunate suppositories



First-line treatment of P. falciparum AS+AQ 2004(unconfirmed)

First-line treatment of P. falciparum AS+AQ 2004(confirmed)





CAM

EROO

N

Adm

itted

case

s per

100

0

0

2

4

6

8

10

12

2001 2002 2003 2004 2005 2006 2007


No data




Reported cases PNLP Surveillance data Insecticide-treated nets (ITN) MICS 2000, DHS 2004, MICS 2006

Operational coverage of ITNs, IRS and access to medicines PNLP Programme report Treatment MICS 2000, DHS 2004, MICS 2006

Financial data PNLP Programme report Use of health services DHS 2004



CAM

EROO

N





No. of ITNs 1 097 510and/or LLINs

No. of first-line 3 583 332treatmentcourses received

No. of ACT treatmentcourses received

Others

Bilaterals

European Union

GFATM 12416102 4472742

World Bank

UN agencies 170000 170000 195000 195000 280000

Gov. malaria 1714290 6626706 7147000 7504000 7880000expenditure




No data


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years withfever who took antimalarial drugs% of children < 5 years withfever who took antimalarial drugssame or next day% of children < 5 years withfever who took ACT% of children < 5 years withfever who took ACT same ornext day

Perc

enta

ge

0

10

20

30

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

5

10

15

20

25

2001 2002 2003 2004 2005 2006 2007


Algeria

Sudan

Niger

Nigeria

Egypt

Chad

I. EpIdEmIologICal profIlE



All age groups 10 468 < 5 years 1 943 19 ≥ 5 years 8 525 81


High transmission ≥ 1/1000 4 862 46Low transmission (0–1/1000) 5 485 52Malaria-free (0 cases) 122 1Rural population 7 779 74


Major Anopheles species: arabiensis, coustani, funestus, nili, pharoensisPlasmodium species: falciparum, vivax



Fever suspected of being malaria All ages 7 095 000 3 965 000 10 400 000 678 379 993 < 5 years 4 301 000 760 000 8 250 000 2 214 391 4 246

Malaria cases All ages 4 179 000 2 335 000 6 126 000 399 223 585 < 5 years 2 533 000 447 000 4 859 000 1 304 230 2 501

Malaria deaths All ages 18 000 8 900 30 000 1.7 0.85 2.9 < 5 years 17 000 8 300 28 000 8.7 4.3 14




Reported malaria 414 149 462 730 463 324 395 001 398 447 400 134 cases, all ages

Reported malaria cases, < 5 years

All-cause outpatient consultations, all ages

All-cause outpatient consultations, < 5 years

Reported malaria 1 872 1 888 1 940 1 630 deaths, all ages

Reported malaria deaths, < 5 years

All-cause deaths, 5 263 5 460 5 756 4 923 all ages

All-cause deaths, < 5 years

Mala

ria d

eath

s per

100

0

0

0.2

0.4

0.6

0.8

1

2001 2002 2003 2004 2005 2006 2007


No data 0 0–1 1–100 > 100


Mala

ria ca

ses p

er 1

000

0

20

40

60

2001 2002 2003 2004 2005 2006 2007


Chad accounts for around 1% of cases in the WHO African Region. Malaria transmission is more intense in the south, occurring seasonally between May and December. Almost all cases are caused by P. falciparum, but most are unconfirmed despite recent improvements in diagnostic services. Numbers of reported cases and deaths have increased in recent years; it is not known whether this reflects a real increase in malaria burden or improved report-ing. The NMP distributed approximately 350 000 ITNs in 2005 and again in 2006, of which 300 000 were LLINs, many fewer than are needed to protect 10 million people at risk. Although IRS is not a national policy, about 40 000 households were sprayed in 2006. Courses of ACT used in 2006 were far below the estimated number of cases. Malaria control has been funded mainly by the government, UN agencies and bilateral organizations.






Targeting – All age groups Yes 1998 Distribution – EPI routine and campaign Yes 2005






Intermittent preventive IPT used to prevent malaria during Yes 2005 IPT implemented countrywide No —treatment (IPT) pregnancy



Parasitological confirmation for all age groups No 2005 Prereferral treatment at health-facility level No —with quinine im or artesunate suppositories



First-line treatment of P. falciparum AS+AQ/AL —(unconfirmed)

First-line treatment of P. falciparum AS+AQ/AL —(confirmed)

Treatment failure of P. falciparum QN(7d) —

Treatment of severe malaria QN(7d) —



Reported malaria 42 221 56 496 58 608 56 908 56 027 52 348admissions (all ages)

Reported malariaadmissions (< 5)



Examined 43 180 44 689 54 381 42 859 50 363 63 815

Positive 38 287 43 933 45 195 31 929 41 237 46 233

P. falciparum

CHA

D

0

2

4

6

8

Adm

itted

case

s per

100

0

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007





Reported cases DSIS Surveillance data Insecticide-treated nets (ITN) MICS 2000, DHS 2004

Operational coverage of ITNs, IRS and access to medicines PNLP Programme report Treatment MICS 2000



IV. FINANCING MALARIA CONTROLGovernmental and external financing Breakdown of expenditure by interventions

% of pregnant womenwho slept under any net

% of pregnant womenwho slept under an ITN

Source: DHS 2004.

No. of households 39 500protected by IRS

No. of ITNs 104 118 10 000 128 293 267 000and/or LLINs

No. of first-line 40 586 104 717treatmentcourses received


Others

Bilaterals 1 160 207 490 487 626 794 348 808 109

European Union

GFATM

World Bank 125 649

UN agencies 106 019 159 746 125 000 634 000 1 921 000 604 000

Gov. malaria 40 000 40 000 40 000 40 000 40 00 40 000expenditure



CHA

D


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

0.5

1

1.5

2

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

0.5

1

1.5

2

2.5

3

2001 2002 2003 2004 2005 2006 2007

No data

No data


Perc

enta

ge

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007

2001 2002 2003 2004 2005 2006 2007


Brazil

Venezuela

Ecuador

Peru

Pacific Ocean

ColombiaColombia had an estimated 408 000 malaria cases in 2006, approximately 20% of all episodes in the WHO Region of the Americas. One quarter (24%)of the population is at risk of malaria, and transmission is highest in the upper Sinú River and lower Cauca River regions, in Urabá, and at the Pacificcoast. A total of 121 000 cases and 106 deaths were reported in 2006, and case numbers have been falling since 2001. Almost all reported cases areconfirmed as malaria, and 28% were P. falciparum in 2007. IRS is implemented selectively, protecting 20 000–30 000 households in 2006–2007. 170000 ITNs were distributed in 2005 and 87 000 in 2007. First-line antimalarial drugs are sufficient to treat all reported cases, and ACT was introducedin 2007. The government has been the principal source of funding for malaria control, with additional support from the Global Fund in 2007.





5 years 41 120 90




Major Anopheles species: albitarsis, albimanus, darlingi, nuneztovariPlasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 1 892 000 1 171 000 2 830 000 42 26 62< 5 years 285 000 177 000 427 000 64 40 96

Malaria cases All ages 408 000 293 000 566 000 9.0 6.0 12< 5 years 62 000 44 000 85 000 14 10 19

Malaria deaths All ages 440 260 660 0.01 0.01 0.01< 5 years 120 70 180 0.03 0.02 0.04





Reported malaria 21 831 13 936 17 849 10 195 18 113 8 831cases, < 5 years

All-cause outpatientconsultations, all ages


Reported malaria 58 40 24 25 28 53deaths, all ages

Reported malaria 4 2 2 3deaths, < 5 years

All-cause deaths,all ages

All-cause deaths,< 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.001

0.002

0.003

2001 2002 2003 2004 2005 2006 2007




Examined 824 780 846 062 520 980 451 240 410 300 451 240 441 556

Positive 206 195 195 719 221 834 116 872 118 163 120 096 107 189

P. falciparum 91 406 87 751 61 975 42 137 38 105 43 547 30 065



Insecticide-treated nets (ITN) Distribution of LLINS – Free No — Distribution – Antenatal care No —







Intermittent preventive IPT used to prevent malaria during No — IPT implemented countrywide No 2006treatment (IPT) pregnancy


Oral artemisinin monotherapies banned No — Home management of malaria No —





First-line treatment of P. falciparum AQ+SP; 2004(confirmed) AS+MQ, AL

Treatment failure of P. falciparum QN(3d)+CL(5d) 2004


Treatment of P. vivax CQ+PQ —


COLO

MBI

A

No data

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007





Reported cases PAHO MCQ Surveillance data Insecticide-treated nets (ITN) DHS 2000

Operational coverage of ITNs, IRS and access to medicines PAHO MCQ Programme report Treatment No surveys

Financial data PAHO MCQ Programme report Use of health services DHS 2004



COLO

MBI

A

No. of households 23 028 132 900 38 295 20 526 20 526 28 728protected by IRS

No. of ITNs 170 000 87 394and/or LLINs

No. of first-line 196 200 178 904 169 816 122 804 145 525 155 132treatmentcourses received


Others 500 000

Bilaterals

European Union

GFATM 3 000 000

World Bank

UN agencies

Gov. malaria 11 363 636 11 363 636 13 049 962 13 702 460 13 702 460 13 702 460 16 000 000expenditure



No data


Perc

enta

ge

0

2

4

6

8

2001 2002 2003 2004 2005 2006 2007

Not applicable

2001 2002 2003 2004 2005 2006 20070

20

40

60

80

100

Perc

enta

ge

WHO 2010 target

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

5

10

15

20

25

2001 2002 2003 2004 2005 2006 2007


Gulf of Guinea

Guinea

Liberia

Ghana

Burkina Faso

Côte d’Ivoire





5 years 16 065 85




Major Anopheles species: arabiensis, brochieri, coustani, funestus, gambiae, hancocki,hargreavesi, melas, moucheti, nili, paludis, pharoensis











Reported malaria 1 116 327 1 231 909 1 107 179 1 095 363 1 051 366 1 075 017cases, all ages



All-cause outpatient 397 679 519 240 450 098 452 086 441 055 465 391consultations, < 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

50

100

150

200

2001 2002 2003 2004 2005 2006 2007


Côte d’Ivoire had an estimated 7 million malaria cases in 2006, 3% of all cases in the WHO African Region. Transmission occurs all year round through-out the country, but is more seasonal in the north. None of the 1.3 million cases reported in 2006 were confirmed as malaria. There was no evidence ofa systematic decline in malaria cases in 2001–2006. The number of deaths increased, perhaps due to improved reporting. IRS is not carried out in Côted’Ivoire. The NMCP distributed only 370 000 LLINs in 2006. Only 27% of households owned a mosquito net in 2006, and just 6% had an ITN. Despitethe adoption of ACT as treatment policy in 2003, a 2006 survey reported that only 3% of febrile children were given ACT. Funding for malaria controlwas around US$ 6 million in 2006, which is not adequate to reach targets for prevention and cure nationwide.

Data not available




Reported malaria 808 1 268 1 365 1 430 1 799 1 806admissions (< 5)






Targeting – All age groups No — Distribution – EPI routine and campaign No —









Parasitological confirmation for all age groups No — Prereferral treatment at health-facility level No —with quinine im or artesunate suppositories





Treatment failure of P. falciparum AL 2003




CÔTE

D’I

VOIR

EAd

mitt

ed ca

ses p

er 1

000

0

0.2

0.4

0.6

0.8

1

2001 2002 2003 2004 2005 2006 2007


No data




Reported cases DIPE Surveillance data Insecticide-treated nets (ITN) MICS 2000, AIS 2005, MICS 2006

Operational coverage of ITNs, IRS and access to medicines DIPE Programme report Treatment MICS 2000, MICS 2006

Financial data DIPE Programme report Use of health services MICS 2006



CÔTE

D’I

VOIR

E



Source: AIS 2005, MICS 2006.



Others

Bilaterals 60 642 60 642 73 8245

European Union

GFATM

World Bank

UN agencies 1 460 519 2 409 826 3 016 659

Gov. malaria 1 129 683 2 352 953 2 341 786 2 427 239expenditure



Source: MICS 2006.

Others

Treatment 846 669 2 184 073 2 139 786 191 955 2 252 520

Diagnosis

IRS

ITN 62 924 186 448 213 788 185 956 214 796 1 304 000


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

2

4

6

8

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with fever who took antimalarial drugs% of children < 5 years with fever who took antimalarial drugssame or next day% of children < 5 years with fever who took ACT% of children < 5 years with fever who took ACTsame or next day

Fund

ing b

y sou

rce (

$m)

0

2

4

6

8

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007

No. of first-line 971 683 1 102 879 721 314treatmentcourses received


Perc

enta

ge

0

2

4

6

2001 2002 2003 2004 2005 2006 2007



Democratic Republic of the Congo





5 years 48 800 80




Major Anopheles species: arabiensis, brochieri, coustani, funestus, gambiae, hancocki,hargreavesi, melas, moucheti, paludis, pharoensis











Reported malaria 5 008 956cases, all ages

Reported malaria 2 380 353cases, < 5 years


All-cause outpatient 2 735 273consultations, < 5 years

Reported malaria 24 347deaths, all ages

Reported malaria 15 032deaths, < 5 years

All-cause deaths, 28 605all ages


No data 0 0–1 1–100 > 100

No data

2001 2002 2003 2004 2005 2006 2007

No data

2001 2002 2003 2004 2005 2006 2007

Cameroon

Kenya

ZambiaAngola

GabonCongo Uganda

This country of 61 million people accounted for an estimated 11% of all malaria cases in the WHO African Region in 2006. Transmission occurs allyear round, though with seasonal variation. Malaria is largely due to P. falciparum, but no cases are confirmed by microscopy. Cases and deaths werereported by the NMCP for 2006 only; routine surveillance has provided no information about how malaria is distributed across the country. IRS is notnational policy. The number of ITNs distributed annually by the NMCP has grown significantly since 2001, reaching 2.4 million in 2006, but this is stillinsufficient to protect 23 million people at risk. A total of 1.7 million doses of first-line medicine were delivered through public facilities in 2006; coveringonly 7% of estimated cases. No data on funding for malaria control were provided by the NMCP.



Reported malaria 139 789admissions (all ages)

Reported malaria 66 957admissions (< 5)

All-cause admissions, 366 702all ages

All-cause admissions, 149 841< 5 years






















DEM

OCR

ATI

CR

EPU

BLIC

OF

THE

CONG

O

No data

2001 2002 2003 2004 2005 2006 2007

No data




Reported cases PNLP database Surveillance data Insecticide-treated nets (ITN) MICS 2001

Operational coverage of ITNs, IRS and access to medicines PNLP database Programme report Treatment MICS 2001

Financial data PNLP database Programme report Use of health services MICS 2001



DEM

OCR

ATI

CR

EPU

BLIC

OF

THE

CONG

O



Source: MICS 2001.


No. of ITNs 200 000 291 825 338 856 877 161 791 135 2 379 384and/or LLINs


No. of ACT treatment 1 681 211courses received



Source: MICS 2001.

No data


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007


No data

Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007



Sudan

Somalia

Kenya

IndianOcean

Gulf of Aden

Ethiopia



No data 0 0–1 1–100 > 100



5 years 67 582 83


High transmission 1/1000 55 094 68Low transmission (0–1/1000) 0 0Malaria-free (0 cases) 25 927 32Rural population 67 879 84


Major Anopheles species: arabiensis, coustani, funestus, nili, paludis, pharoensis,quadriannulatus




Fever suspected of being malaria All ages 44 965 000 11 108 000 94 175 000 555 137 1 162< 5 years 7 514 000 1 856 000 15 737 000 559 138 1 171

Malaria cases All ages 12 405 000 4 236 000 24 341 000 153 52 300< 5 years 2 073 000 708 000 4 068 000 154 53 303









Reported malaria 1 681 1 607 2 138 3 327 1 086 1 357 991deaths, all ages




Mala

ria ca

ses p

er 1

000

0

50

100

150

200

250

300

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.02

0.04

0.06

0.08

0.1

2001 2002 2003 2004 2005 2006 2007



Ethiopia had approximately 6% of malaria cases in the African Region in 2006. Malaria is present everywhere except the central highlands. Epidemicsare frequent, the last in 2003–2004. Over half of the cases are caused by P. falciparum. The NMCP distributed 20 million LLINs between 2005 and 2007,targeting 40 million people at risk. The percentage of households with one ITN increased from 3% nationwide in 2005 to 53% in 2007. Nearly 7 millioncourses of ACT were delivered in 2006 and 4 million in 2007. Funding increased from US$ 2.7 million in 2001 to over US$ 120 million in 2006, provided bythe government and other donors. A total of 1.2 million cases were reported in 2007, the lowest number in the period 2001–2007. The recent fall in casescoincides with the rapid expansion of control efforts, although intervention effects are not distinguishable from postepidemic decline in NMCP data.




Reported malaria 3 931 4 054 4 747 7 556 9 822 6 204admissions (< 5)



Examined 924 823 829 760 593 476 1 039 641 1 110 988 1 103 120 1 305 859

Positive 318 771 305 328 203 242 412 609 478 311 509 804 604 960

P. falciparum 231 827 195 497 109 140 270 931 315 918 297 766 384 564






Indoor residual spraying (IRS) DDT is used for IRS (public health) only Yes 1998 DDT is used alternately with other Yes —insecticides in the same area

Insecticide-resistance management Yes — IRS is the primary vector-control intervention Yes 1998implemented






Parasitological confirmation for all age groups Yes 1998 Prereferral treatment at health-facility level Yes —with quinine im or artesunate suppositories







Treatment of P. vivax CQ —


ETH

IOPI

AAd

mitt

ed ca

ses p

er 1

000

0

0.2

0.4

0.6

0.8

1

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007



ETH

IOPI

AIII. IMPLEMENTING MALARIA CONTROLCoverage of ITN: survey data Coverage of IRS and ITN: programme data


Reported cases Regional Health Bureau Surveillance data Insecticide-treated nets (ITN) DHS 2000, DHS 2005, MIS 2007

Operational coverage of ITNs, IRS and access to medicines NMCP Programme report Treatment DHS 2000, DHS 2005, MIS 2007

Financial data NMCP Programme report Use of health services DHS 1997



% of pregnant women 2 37who slept under any net

% of pregnant women 1 35who slept under an ITN

Source: DHS 2005, MIS 2007 provisional report.

No. of households 592 197 765 380 859 637 845 693 782 581 1 196 897 1 590 964protected by IRS

No. of ITNs 280 000 320 000 430 000 4 243 157 9 070 718 7 178 443and/or LLINs


No. of ACT treatment 25 000 3 193 993 6 806 744 4 032 640courses received

Others 2 054 9 120 0 8 444 630 703 14 639 958 21 442 875

Bilaterals 131 258 245 694 279 827 1 624 311 685 738 0

European Union 0 0 0 0 0 0 0

GFATM 0 0 0 21 757 639 71 421 627 58 609 473

World Bank 0 0 12 500 11 120 695 037 15 128 000 14 699 888

UN agencies 543 249 509 942 1 406 421 1 602 052 620 894 14 128 937 5 593 555

Gov. malariaexpenditure

% of children 19 22< 5 years with fever


Source: DHS 2005, MIS 2007 provisional report.


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


0

20

40

60

80

100

Perc

enta

ge

WHO 2010 target

2001 2002 2003 2004 2005 2006 2007

0

5

10

15

Perc

enta

ge

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with fever who took antimalarial drugs% of children < 5 years with fever who took antimalarial drugs same or next day% of children < 5 years with fever who took ACT% of children < 5 years with fever who took ACT same or next day

Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Others 28 154 041

Treatment

Diagnosis 22 094 054

IRS 4 320 222

ITN 52 228 038

Fund

ing b

y sou

rce (

$m)

0

50

100

150

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

50

100

150

2001 2002 2003 2004 2005 2006 2007


Atlantic Ocean

Côte d’Ivoire

Benin

NigeriaTogo

Burkina FasoMali

Ghana





5 years 19 813 86




Major Anopheles species: brochieri, coustani, flavicosta, funestus, gambiae, hancocki,hargreavesi, melas, nili, paludis, pharoensis












Reported malaria 856 872 705 288 920 140 1 289 874 2 876 465 789 952 1 056 331cases, < 5 years


All-cause outpatient 1 518 970 1 679 257 1 900 809 1 318 900 1 712 728consultations, < 5 years


Reported malaria 1 441 1 360 1 376 1 354 922 805 1 241deaths, < 5 years



No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

200

400

600

800

1000

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


Ghana had an estimated 7.2 million malaria cases in 2006, 3% of the total for the WHO African Region. Most cases are caused by P. falciparum but only15–20% are confirmed. There was no evidence of a reduction in malaria cases between 2001 and 2007, and reported deaths have increased in 2007.IRS protected 134 000 and 154 000 households in 2006 and 2007 respectively in selected areas. The NMCP programme distributed 3.6 million LLINs in2006–2007. 30% of households owned a mosquito net in 2006, but only 19% owned at least one ITN. 61% of febrile children received an antimalarialdrug, but only 4% were given ACT. Funding for malaria control was close to US$ 60 million in 2006 and US$ 40 million in 2007, provided by government,the Global Fund, the World Bank and bilateral donors.






All-cause admissions, 102 397 100 895 120 126 123 384 174 522 97 860 113 952<5 years



Insecticide-treated nets (ITN) Distribution of LLINS – Free No 2006 Distribution – Antenatal care Yes 1999









Oral artemisinin monotherapies banned Yes 2006 Home management of malaria Yes —

Parasitological confirmation for all age groups No — Prereferral treatment at health-facility level Yes —with quinine im or artesunate suppositories









GHA

NA

Adm

itted

case

s per

100

0

0

5

10

15

20

25

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Examined

Positive 475 441 655 093 472 255 476 484

P. falciparum




Reported cases NMCP, HMIS Surveillance data Insecticide-treated nets (ITN) DHS 2003, MICS 2006

Operational coverage of ITNs, IRS and access to medicines NMCP, Anglo Gold Ashanti (for IRS) Programme report Treatment DHS 2003, MICS 2006

Financial data NMCP, HMIS Programme report Use of health services DHS 2003



GHA

NA








Others 0 13 233

Bilaterals 12 530 000 17 000 000

European Union 70 000

GFATM 21 762 030 15 527 108

World Bank 0 5 000 000

UN agencies 4 000 22 840

Gov. malaria 24 830 000 0expenditure





Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


0

5

10

15

20

Perc

enta

ge

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

20

40

60

2001 2002 2003 2004 2005 2006 2007

No data


Pakistan

Afghanistan

Myanmar

Thailand

China

India




All age groups 1 151 751< 5 years 126 843 11

5 years 1 024 908 89




Major Anopheles species: culicifacies, dirus, fluviatilis, minimus, philippinensis, stephensPlasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 635 991 000 556 475 000 883 196 000 552 483 767< 5 years 54 758 000 47 912 000 76 042 000 432 378 599

Malaria cases All ages 10 650 000 9 003 000 12 414 000 9.0 8.0 11< 5 years 917 000 775 000 1 069 000 7.0 6.0 8.0






Reported malariacases, < 5 years



Reported malaria 1 015 973 1 006 949 963 1 708 1 173deaths, all ages

Reported malariadeaths, < 5 years



No data 0 0–1 1–100 > 100

India had an estimated 10.6 million malaria cases in 2006 that account for approximately 60% of cases in the WHO South-East Asia Region. With over100 million slides examined every year, all reported cases are confirmed; about half are due to P. falciparum. However, the percentage of cases detectedthrough active versus passive surveillance is not known. The states most affected are Uttar Pradesh, Bihar, Karnataka, Orissa, Rajasthan, MadhyaPradesh and Pondicherry. A survey carried out in 2005–2006 found that 36% of households owned a mosquito net. IRS has been the main method ofmosquito control covering about 80 million households and protecting 40% of the population at risk. The programme delivered 8.5 million ITNs, morethan 3 million first-line treatments and 800 000 courses of ACT during 2006 and 2007. Funding increased to more than US$ 140 million in 2007, providedby government, the Global Fund and the World Bank.

Mala

ria ca

ses p

er 1

000

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.001

0.002

0.003

0.004

2001 2002 2003 2004 2005 2006 2007








Indoor residual spraying (IRS) DDT is used for IRS (public health) only Yes — DDT is used alternately with other No —insecticides in the same area

Insecticide-resistance management Yes — IRS is the primary vector-control intervention Yes —implemented





Oral artemisinin monotherapies banned Yes — Home management of malaria Yes —

Parasitological confirmation for all age groups Yes — Prereferral treatment at health-facility level Yes —with quinine im or artesunate suppositories



First-line treatment of P. falciparum CQ+PQ 2007(unconfirmed)

First-line treatment of P. falciparum AS+AP 2007(confirmed)


Treatment of severe malaria QN/AM 2007



Examined 90 389 019 91 617 725 99 136 143 97 111 526 104 120 792 106 606 703 94 855 005

Positive 2 085 484 1 841 227 1 869 403 1 915 363 1 816 569 1 785 109 1 476 562

P. falciparum 1 005 236 897 446 857 101 890 152 805 077 838 555 725 502


INDI

A

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


No data




Reported cases Surveillance data Insecticide-treated nets (ITN) DHS 2005-06

Operational coverage of ITNs, IRS and access to medicines Programme report Treatment MICS 2000




INDI

A



Source: DHS 2005–2006.

No. of households 92 732 282 75 864 024 60 425 231 64 520 714 73 282 197 88 334 429 88 815 998protected by IRS

No. of ITNs 175 000 90 000 230 000 1 200 000 2 720 000 1 495 000 7 000 000and/or LLINs

Others

Bilaterals

European Union

GFATM 912 325 8 227 900 15 727 050

World Bank 16 266 608 13 969 726 18 535 966 1 372 056 9 512 474 22 400 000 29 500 000

UN agencies

Gov. malaria 50 495 000 53 750 000 52 000 000 54 750 000 72 397 500 87 895 000 99 010 000expenditure

Others

Treatment 19 581 760 21 297 213 25 670 343 37 852 753 39 000 000

Diagnosis 9 830 000 10 046 056 13 393 140 30 343 013 38 183 775

IRS 26 899 863 24 316 398 34 974 385 42 890 261 45 585 150

ITN 450 000 258 000 6 256 000 3 737 500 18 340 000




Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


No data

Fund

ing b

y sou

rce (

$m)

0

50

100

150

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

50

100

150

2001 2002 2003 2004 2005 2006 2007


Indian OceanUnited Republic of Tanzania

Uganda

Sudan Ethiopia

Somalia

Kenya





5 years 30 392 83




Major Anopheles species: arabiensis, funestus, gambiae, melas, nili, paludis, pharoensisPlasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 30 193 000 11 746 000 52 746 000 826 321 1 443< 5 years — — —

Malaria cases All ages 11 342 000 4 785 000 19 064 000 310 131 522< 5 years — — —









No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

500

1000

1500

2001 2002 2003 2004 2005 2006 2007


No data

Kenya had an estimated 11.3 million malaria cases in 2006. The majority of cases are caused by P. falciparum but most are unconfirmed. The numberof reported cases increased in 4 out of 5 years between 2001 and 2006; it is not known whether this represents improved reporting or an increase inincidence. No reports of malaria deaths were provided over this period, though estimates suggest that 27 000 people died in 2006. ITN covered 65% ofthe people at risk in 2006. The NMCP distributed 7.1 million ITNs in 2006, of which 6.3 million were LLINs. 5 million ACT courses were provided in 2006,far fewer than would be needed to treat all fever cases. Funding for malaria control was US$ 70 million in 2005 and US$ 46 million in 2006, providedmostly by the Global Fund and bilateral donors.



Examined 43 643 96 893 59 995

Positive 20 049 39 383 28 328

P. falciparum 20 049 39 383 28 328











Case management ACT is free or highly subsidized in public Yes 2006 Free malaria diagnosis and first-line No —sectors treatment of malaria


Parasitological confirmation for all age groups No — Prereferral treatment at health-facility level Yes 2006with quinine im or artesunate suppositories









KEN

YA

Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


No data




Reported cases NMCP Surveillance data Insecticide-treated nets (ITN) MICS 2000, DHS 2003

Operational coverage of ITNs, IRS and access to medicines NCMP Programme report Treatment MICS 2000, DHS 2003

Financial data NMCP Programme report Use of health services DHS 2003



KEN

YA



Source: DHS 2003.

No. of households 60 000 93 000 110 000 691 841protected by IRS

No. of ITNs 120 010 5 550 563 643 218 1 169 600 3 655 576 7 102 752and/or LLINs

No. of first-line 723 333 5 049 000treatmentcourses received

No. of ACT treatment 723 333 5 049 000courses received

Others 0 0 0 0 441 400 119 971

Bilaterals 0 0 0 5 563 684 14 525 404 6 236 657

European Union 0 0 0 0 0 0

GFATM 0 0 3 976 069 0 53 698 910 39 858 515

World Bank 0 0 0 0 0 0

UN agencies 47 210 830 802 672 911 30 000

Gov. malaria 27 631 774 984 84 882 1 233 505 379 494 308 660expenditure



Source: DHS 2003.


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

WHO 2010target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007


Others

Treatment 16 100 000

Diagnosis

IRS 3 800 000

ITN 17 500 000

Fund

ing b

y sou

rce (

$m)

0

20

40

60

80

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Indian Ocean

Mozambique

Madagascar





5 years 16 017 84




Major Anopheles species: arabiensis, coustani, flavicosta, funestus, gambiae, merus,pharoensis






Malaria cases All ages 643 000 163 000 1 286 000 34 9.0 67< 5 years 234 000 59 000 469 000 74 19 149

Malaria deaths All ages 2 300 630 4 200 0.12 0.03 0.22< 5 years 1 800 490 3 300 0.57 0.16 1.1




Reported malaria 1 356 520 1 598 818 2 198 035 1 458 428 1 227 632 1 012 639 790 510cases, all ages



All-cause outpatient 2 307 873 2 505 497 2 572 599 2 647 870 2 817 099 1 845 989consultations, < 5 years

Reported malaria 1 300 1 268 1 091 1 194 1 407 590 371deaths, all ages




No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

100

200

300

400

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.1

0.2

0.3

2001 2002 2003 2004 2005 2006 2007


Transmission occurs all year round in the north, with stronger seasonal peaks between September and June elsewhere. Reported malaria cases droppedfrom 2.2 million in 2003 to 790 000 in 2007, and there was a marked fall in malaria deaths over the same period, perhaps in response to the growinguse of ITNs, IRS and ACT. The NMCP distributed nearly 5 million LLINs over the period 2006–2007, covering half the target population. IRS has alsoincreased since 2003, covering 250 000 households and protecting 1.4 million people at risk in 2006. ACT became more widely available in 2007.Funding for malaria control increased every year in 2003–2006; support from the Global Fund, World Bank, UN and bilateral agencies exceeded US$53 million in 2006.





All-cause admissions, 311 952 283 726 432 123 413 470 407 361all ages


Examined 31 354 27 752 37 753 39 174 37 943

Positive 7 838 5 273 6 796 7 834 7 588

P. falciparum






Indoor residual spraying (IRS) DDT is used for IRS (public health) only No 2005 DDT is used alternately with other No 2005insecticides in the same area




Intermittent preventive IPT used to prevent malaria during Yes 2004 IPT implemented countrywide No 2004treatment (IPT) pregnancy



Parasitological confirmation for all age groups Yes 2006 Prereferral treatment at health-facility level No 2006with quinine im or artesunate suppositories









MA

DAGA

SCA

RAd

mitt

ed ca

ses p

er 1

000

0

0.5

1

1.5

2

2.5

3

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007






MA

DAGA

SCA

R




No. of households 174 386 102 400 250 000 250 000 248 269protected by IRS


Others 29 190 207

Bilaterals 475 000

European Union

GFATM 2 000 063 6 744 844 31 927 971 12 786 722

World Bank 90 000

UN agencies 12 751 000


No data


Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

No data

Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with feverwho took antimalarial drugs% of children < 5 years with feverwho took antimalarial drugssame or next day% of children < 5 years with feverwho took ACT% of children < 5 years with feverwho took ACT same or next day


Reported cases sss:minsanpfps Surveillance data Insecticide-treated nets (ITN) MICS 2000, DHS 2003–2004

Operational coverage of ITNs, IRS and access to medicines sss:minsanpfps Programme report Treatment MICS 2000, DHS 2003–2004

Financial data sss:minsanpfps Programme report Use of health services DHS 2003



Source: DHS 2003.

Fund

ing b

y sou

rce (

$m)

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

2001 2002 2003 2004 2005 2006 2007




IndianOcean

United Republic of Tanzania

Zambia

Mozambique

Zimbabwe

Malawi





5 years 11 145 82




Major Anopheles species: coustani, funestus, gambiae, paludis, pharoensisPlasmodium species: falciparum, vivax












All-cause outpatient 15 753 331 14 014 893consultations, all ages






No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

50

100

150

200

250

300

350

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


Malaria is endemic in all parts of the country, with seasonal peaks between December and June. The majority of cases are caused by P. falciparum,but most are not confirmed. There is no evidence of a decline in malaria cases and deaths over the period 2001–2006, despite improvements in thecoverage of ITNs. The NMCP distributed over 4.6 million ITNs between 2003 and 2006, and a 2006 survey found that 36% of households had at leastone ITN. In 2006, 24% of febrile children under 5 years were treated with an antimalarial; ACT was adopted as the recommended method of treatmentonly in 2007. US$ 23 million was spent on malaria control in 2006, provided by government, the Global Fund, the World Bank and UN agencies.




Reported malaria 17 466admissions (< 5)












Intermittent preventive IPT used to prevent malaria during Yes — IPT implemented countrywide Yes —treatment (IPT) pregnancy



Parasitological confirmation for all age groups No — Prereferral treatment at health-facility level Yes —with quinine im or artesunate suppositories





Treatment failure of P. falciparum AS+AQ 2007




MA

LAW

I

Adm

itted

case

s per

100

0

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


No data









No. of ITNs 46 062 185 968 1 029 884 1 295 498 815 620 1 508 735and/or LLINs

Others

Bilaterals

European Union

GFATM 6 300 000

World Bank 3 000 000

UN agencies 2 700 000

Gov. malaria 12 000 000expenditure


Reported cases HMIS /IDSR Surveillance data Insecticide-treated nets (ITN) DHS 2000, DHS 2004, MICS 2006

Operational coverage of ITNs, IRS and access to medicines HMIS /NMCP Programme report Treatment DHS 2000, DHS 2004, MICS 2006

Financial data HMIS /NMCP Programme report Use of health services DHS 2004

MA

LAW

I


Perc

enta

ge

0

10

20

30

40

50

60

70

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

2001 2002 2003 2004 2005 2006 2007






Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Others

Treatment 2 000 000

Diagnosis

IRS 50000

ITN 5 000 000

Fund

ing b

y sou

rce (

$m)

0

5

10

15

20

25

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

50

100

150

200

2001 2002 2003 2004 2005 2006 2007

80



Mauritania

Algeria

NigerSenegal

GuineaBurkina Faso Nigeria

Mali





5 years 9 721 81




Major Anopheles species: arabiensis, brochieri, coustani, flavicosta, funestus, gambiae,hancocki, nili, paludis, pharoensis















Reported malaria 562 826 1 309 1 012 1 285 1 914deaths, all ages


All-cause deaths, 17 925 2 561 3 095 2 664 3 896 5 132all ages

All-cause deaths, 7 303 999 1 461 1 073 1 637 2 207< 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

100

200

300

400

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


Mali had an estimated 4.3 million malaria cases in 2006, 2% of the total for the WHO African Region. Malaria transmission is more intensive in the southernpart of the country with seasonal peaks between May and November. Almost all cases are caused by P. falciparum, but the number confirmed is unknowndespite recent improvements in diagnostic services. An exceptionally large number of cases was reported in 2004; reported deaths increased more thanthreefold between 2001 and 2006. In 2005 and 2006 combined, the NMCP distributed nearly 700 000 LLINs. However, this is still inadequate to cover12 million people at risk. IRS is not national policy. Although Mali has adopted ACT, there are no data on the number of people treated. Government expendi-ture on malaria control is unknown, and no details have been given of disbursement from the Global Fund grant awarded in 2001 and 2006.


MA

LI












Case management ACT is free or highly subsidized in public Yes — Free malaria diagnosis and first-line No —sectors treatment of malaria

Oral artemisinin monotherapies banned No — Home management of malaria Yes —






Treatment failure of P. falciparum AS+SP 2004




No data No data


MA

LIIII. IMPLEMENTING MALARIA CONTROLCoverage of ITN: survey data Coverage of IRS and ITN: programme data





Source: DHS 2001, DHS 2006.


No. of ITNs 572 556 90 900and/or LLINs

Others

Bilaterals

European Union

GFATM 2592990

World Bank

UN agencies



Reported cases SLIS Surveillance data Insecticide-treated nets (ITN) DHS 2001, DHS 2006

Operational coverage of ITNs, IRS and access to medicines SLIS Programme report Treatment DHS 2001, DHS 2006

Financial data SLIS Programme report Use of health services DHS 2006



Source: DHS 2001, DHS 2006.


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


No data

Fund

ing b

y sou

rce (

$m)

0

1

2

3

2001 2002 2003 2004 2005 2006 2007

No data


Zambia

South Africa

Botswana

ZimbabweMadagascar

Indian Ocean

Mozambique





5 years 17 301 82




Major Anopheles species: funestus, s.l., gambiae, s.l.Plasmodium species: falciparum, vivax


















No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

100

200

300

400

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.1

0.2

0.3

0.4

0.5

2001 2002 2003 2004 2005 2006 2007


Mozambique had an estimated 7.4 million malaria cases in 2006. Transmission is seasonal, and mainly between November and July. The majority of malariacases are caused by P. falciparum but most are unconfirmed. Reported cases increased between 2001 and 2006; reported deaths fluctuated around anaverage of 4000 (compared with 23 000 estimated). IRS has been a principal method of mosquito control, covering 1.5 million households in 2006 (37%of people at risk). The NMCP distributed about 700 000 ITNs in each of 2005 and 2006, far fewer than are needed to protect the 18 million people at risk. A2007 survey reported that 37% of households owned a net, but only 16% had an ITN. ACT has been used since 2006 as a second-line treatment. The NMCPhas provided no information about funding, though awards have been made by the Global Fund, the World Bank and other donors.





Insecticide-treated nets (ITN) Distribution of LLINS – Free Yes — Distribution – Antenatal care Yes —



Indoor residual spraying (IRS) DDT is used for IRS (public health) only Yes 2005 DDT is used alternately with other No — insecticides in the same area

Insecticide-resistance management No — IRS is the primary vector-control intervention Yes —implemented


Where IRS is conducted, other options Yes 1999 are also implemented, e.g. ITN




Parasitological confirmation for all age groups No — Prereferral treatment at health-facility level No — with quinine im or artesunate suppositories





Treatment failure of P. falciparum AS+AQ1 2004




MOZ

AM

BIQU

E

No data No data




Reported cases Surveillance data Insecticide-treated nets (ITN) DHS 2003 (National report)

Operational coverage of ITNs, IRS and access to medicines Programme report Treatment DHS 2003 (National report)




MOZ

AM

BIQU

E



Source: DHS 2003 (National report).

No. of households 584 830 466 520 434 948 754 492 1 250 375 1 537 825protected by IRS

No. of ITNs 102 277 130 326 201 492 401 802 725 119 683 410and/or LLINs



Source: DHS 2003 (National report).

No data


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

No data

Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years withfever who took antimalarial drugs% of children < 5 years withfever who took antimalarial drugssame or next day% of children < 5 years withfever who took ACT% of children < 5 years withfever who took ACT same or nextday

No data


Myanmar





5 years 44 234 91




Major Anopheles species: dirus, jeyporiensis, maculatus, minimus, philippinensis,sundaicus



Thailand

China

India

Bay of Bengal




Malaria cases All ages 4 209 000 867 000 8 529 000 87 18 176< 5 years 494 000 102 000 1 002 000 119 25 242

Malaria deaths All ages 9 100 2 400 17 000 0.19 0.05 0.35< 5 years 2 300 590 4 400 0.56 0.14 1.1




Reported malaria 574 352 721 739 716 806 602 883 506 041 475 297cases, all ages



All-cause outpatient 1 079 481 645 782 667 332 698 782 734 521 691 546consultations, < 5 years




All-cause deaths, 1 791 2 546 1 969 1 995 1 734 1 638< 5 years

No data 0 0–1 1–100 > 100

Myanmar had an estimated 4.2 million malaria cases in 2006, about 20% of all cases in the WHO South-East Asia Region. More than half of the suspectedmalaria cases are unconfirmed. Among those that are identified as malaria, 70% or more are caused by P. falciparum. Although much of the population is at riskof malaria, most vulnerable are non-immune migrant workers occupied with gem-mining in forests, logging, agriculture and construction. Reported numbers ofcases and deaths were falling over the period 2002–2006, perhaps representing a real decline in the malaria burden. IRS has been used selectively. The numberof ITNs distributed increased every year between 2001 and 2006, but population coverage is 5% or less. With increased support from UN agencies and otherdonors in 2005 and 2006, the availability of first-line drugs and ACT appears to have improved, and may be enough to treat all reported cases.

Mala

ria ca

ses p

er 1

000

0

50

100

150

200

250

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

2001 2002 2003 2004 2005 2006 2007




Reported malaria 9 301 10 610 7 470 8 615 9 147 7 974admissions (< 5)

All-cause admissions, 591 546 612 823 602 178 600 939 650 417 643 594all ages

All-cause admissions, 61 296 70 639 63 738 76 002 81 201 80 138< 5 years

Examined 463 194 467 871 481 201 432 581 424 652 478 066

Positive 170 502 173 096 177 530 152 070 151 508 200 679

P. falciparum 130 029 133 187 138 187 114 523 111 357 144 585




Targeting – All age groups Yes — Distribution – EPI routine and campaign No —


Indoor residual spraying (IRS) DDT is used for IRS (public health) only Yes — DDT is used alternately with other No —insecticides in the same area







Parasitological confirmation for all age groups Yes — Prereferral treatment at health-facility level Yes —with quinine im or artesunate suppositories



First-line treatment of P. falciparum CQ 2002(unconfirmed)

First-line treatment of P. falciparum AS+MQ; AL; 2002(confirmed) DHA+PIP

Treatment failure of P. falciparum QN+T or D 2002

Treatment of severe malaria QN/AS 2002




MYA

NM

AR

Adm

itted

case

s per

100

0

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

120

140

2001 2002 2003 2004 2005 2006 2007



MYA

NM

ARIII. IMPLEMENTING MALARIA CONTROL





No. of ITNs 46 903 47 329 137 636 181 072 222 886 538 436and/or LLINs

Others

Bilaterals 500 000 122 942 200 000

European Union

GFATM 1 900 000

World Bank

UN agencies 1 204 528 1 120 000

Gov. malaria 105 859 129 219expenditure


Reported cases NMCP Surveillance data Insecticide-treated nets (ITN) No surveys

Operational coverage of ITNs, IRS and access to medicines NMCP Programme report Treatment No surveys

Financial data NMCP Programme report Use of health services MICS 2000

No data

No data

No. of first-line 433 135 1 000 710treatmentcourses received



Perc

enta

ge

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007

No data

Perc

enta

ge

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007


Mali

Chad

Algeria

BurkinaFaso Nigeria

Niger





5 years 11 024 80




Major Anopheles species: arabiensis, coustani, funestus, gambiae, moucheti, nili,pharoensis







Malaria deaths All ages 32 000 15 000 53 000 2.3 1.1 3.9< 5 years 30 000 14 000 51 000 11 5.2 18




Reported malaria 1 340 142 888 345 681 783 760 718 817 707 886 531 1 308 234cases, all ages



All-cause outpatient 2 137 498 2 080 927 1 847 222 731 299 833 437 1 627 033 1 957 624consultations, < 5 years


Reported malaria 1 019 1 024 907 1 607 1 793 1 531 1 042deaths, < 5 years



No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

50

100

150

200

250

300

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.2

0.4

0.6

0.8

1

2001 2002 2003 2004 2005 2006 2007


Malaria transmission is more intensive in the south, occurring seasonally between January and April. Desert areas in the north are malaria-free. Almostall cases are caused by P. falciparum but most cases are unconfirmed. Reported cases and deaths fluctuated over the period 2001–2006, but therewere no systematic trends. IRS is not national policy. In 2005, the NMCP distributed nearly 2.7 million ITNs. DHS 2006 reported that 69% of householdsowned a mosquito net and 43% an ITN, but only 7% of children under 5 years slept under an ITN. Despite the adoption of ACTs in 2005, a 2006 surveyrecorded no children treated with ACT. Only one third of children with fever were given antimalarial medicine. The NMCP has provided little informationabout funding, but reported a major award from the Global Fund in 2004.


Reported malaria 1 665 7 773 17 938admissions (all ages)

Reported malaria 5 888 3 179 6 818admissions (< 5)

All-cause admissions, 15 979 40 850 75 973all ages

All-cause admissions, 20 711 10 016 22 880< 5 years

Examined 81 814 107 092 87 103 204 226

Positive 56 460 76 030 46 170 55 141

P. falciparum



Insecticide-treated nets (ITN) Distribution of LLINS – Free No — Distribution – Antenatal care Yes 2005









Oral artemisinin monotherapies banned No — Home management of malaria Yes 1998











NIG

ER

Adm

itted

case

s per

100

0

0

0.5

1

1.5

2

2.5

3

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

50

100

150

2001 2002 2003 2004 2005 2006 2007





Reported cases MOH/PNLP/SNIS Surveillance data Insecticide-treated nets (ITN) MICS 2000, DHS 2006

Operational coverage of ITNs, IRS and access to medicines MOH/PNLP/SNIS Programme report Treatment MICS 2000, DHS 2006

Financial data MOH/PNLP/SNIS Programme report Use of health services DHS 2006



NIG

ER



Source: DHS 2006.


No. of ITNs 2 665 000and/or LLINs



Others 210 852

Bilaterals

European Union 0

GFATM 11 257 988

World Bank 0

UN agencies 313 775

Gov. malaria 25 000 25 000 8 846 444 231 342 346expenditure



Source: DHS 2006.

No data


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with fever who tookantimalarial drugs% of children < 5 years with fever who tookantimalarial drugssame or next day% of children < 5 years with fever who took ACT% of children < 5 years with fever who took ACTsame or next day

Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Gulf of Guinea

Chad

Cameroon

Benin

Niger

Nigeria





5 years 120 217 83




Major Anopheles species: arabiensis, brochieri, coustani, flavicosta, funestus, gambiae,hancocki, hargreavesi, melas, moucheti, nili, paludis, pharoensis






Malaria cases All ages 57 506 000 35 481 000 80 297 000 397 245 555< 5 years 34 096 000 5 710 000 65 825 000 1 392 233 2 686






Reported malaria 171 812 507 173 814 274 865 374 1 004 392cases, < 5 years




Reported malaria 721 1 714 2 096 2 001 2 695deaths, < 5 years

All-cause deaths, 7 632 13 504 8 747 12 013all ages

All-cause deaths, 1 501 3 649 5 581 3 612 4 495< 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


Nigeria accounts for a quarter of all malaria cases in the WHO African Region. Transmission in the south occurs all-year round, and is more seasonal in thenorth. Almost all cases are caused by P. falciparum but most are unconfirmed. There is no evidence of a systematic decline in malaria burden; the upwardtrend in numbers of cases and deaths is probably due to improvements in reporting. IRS is not national policy. The NMCP delivered about 17 million ITNduring 2005–2007 (6.6 million LLIN), enough to cover only 23% of the population at risk. The programme delivered 4.5 million courses of ACT in 2006 and9 million in 2007, far below total requirements. Funding for malaria control was reported to have increased from US$ 17 million in 2005 to US$ 60 million in2007, provided by the government, the Global Fund and the World Bank. This is unlikely to be sufficient to reach national targets for prevention and cure.

Mala

ria d

eath

s per

100

0

0

0.05

0.1

0.15

2001 2002 2003 2004 2005 2006 2007



Reported malaria 5 935 41 913 80 825 102 303 121 696admissions (all ages)

Reported malaria 2 358 12 814 21 455 31 151 36 647admissions (< 5)

All-cause admissions, 96 074 342 748 614 272 675 044 747 193all ages

All-cause admissions, 32 101 102 152 186 861 212 596 211 559< 5 years























NIG

ERIA

Adm

itted

case

s per

100

0

0

0.5

1

1.5

2

2.5

2001 2002 2003 2004 2005 2006 2007


No data







Source: DHS 2003.

No. of households 900 4 500protected by IRS

No. of ITNs 200 000 218 900 696 482 3 279 000 5 086 934 8 853 589 3 225 594and/or LLINs

Others 2 020 000 1 850 000 2 330 000

Bilaterals

European Union

GFATM 15 000 000 16 000 000 20 000 000

World Bank 2 000 000 28 700 000

UN agencies 8

Gov. malaria 2 020 000 4 000 000 3 530 000 390 625 1 953 125 10 000 000 11 000 000expenditure


Reported cases Epid Division,FMOH Surveillance data Insecticide-Treated Nets (ITN) DHS 2003

Operational coverage of ITNs, IRS and access to medicines Epid Division,FMOH Programme report Treatment DHS 2003

Financial data Epid Division,FMOH Programme report Use of health services DHS 2003

NIG

ERIA





Source: DHS 2003.

Others

Treatment 4 038 461

Diagnosis

IRS

ITN 3 461 538


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

2

4

6

8

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

20

40

60

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007


Indian Ocean

China

India

Afghanistan

Pakistan





5 years 141 931 88




Major Anopheles species: culicifacies, stephensiPlasmodium species: falciparum, vivax



Fever suspected of being malaria All ages 61 212 000 43 177 000 86 832 000 380 268 5405 years 26 357 000 18 591 000 37 388 000 1 386 978 1 967

Malaria cases All ages 1 499 000 1 145 000 2 012 000 9.0 7.0 13< 5 years 645 000 493 000 866 000 34 26 46

Malaria deaths All ages 1 400 850 2 000 0.01 0.01 0.01< 5 years 660 410 970 0.04 0.02 0.05






All-cause outpatientvconsultations, all ages


No data 0 0–1 1–100 > 100


Reported malaria 29 52 9 24deaths, all ages




Following the resurgence of malaria after the eradication programme of the 1970s, malaria has been a persistent problem in Pakistan. There were anestimated 1.5 million malaria episodes in 2006, accounting for one quarter of all cases in the WHO Eastern Mediterranean Region. Most cases occurbetween July and November. Almost all are confirmed as malaria, and about 30% are due to P. falciparum. Routinely-collected data suggest thatincidence was roughly stable between 2001 and 2007. Deaths are reported sporadically. IRS has been used selectively and covered about 300 000households in 2006. The NMCP delivered 240 000 LLINs in 2006, far fewer than needed to protect the population at risk. Information about the provisionof antimalarial medicines has not been provided by the NMCP. Government funding for malaria control has been approximately US$ 1 million annuallysince 2002, boosted by Global Fund grants in 2004 and 2005.

Mala

ria ca

ses p

er 1

000

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

2001 2002 2003 2004 2005 2006 20070

0.0001

0.0002

0.0003

0.0004

0.0005Reported malaria deaths/1000, all agesReported malaria deaths/1000, < 5



Examined 3 572 425 3 399 524 4 577 037 4 243 108 4 776 274 4 490 577 4 905 561

Positive 125 292 107 666 125 152 126 719 127 826 124 910 128 570

P. falciparum 41 771 32 591 39 944 32 761 42 056 37 837 39 856
















First-line treatment of P. falciparum AS+SP —(unconfirmed)

First-line treatment of P. falciparum AS+SP —(confirmed)

Treatment failure of P. falciparum QN —

Treatment of severe malaria QN/AM —



PAK

ISTA

N

No data

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007





Reported cases Surveillance data Insecticide-treated nets (ITN) No surveys

Operational coverage of ITNs, IRS and access to medicines Programme report Treatment No surveys




PAK

ISTA

N



Others 2230000 1420000

Bilaterals

European Union

GFATM 4500000 4407000

World Bank

UN agencies 100000 76841 84450 67000


Others

Treatment 23400 21200 13750 12000 16800 16650

Diagnosis 3300000 25000 10900 13500 18000 22000

IRS 200000 177000 208500 190000 207451 250067

ITN 57000 191000 175099

No data


Perc

enta

ge

0

0.5

1

1.5

2

2001 2002 2003 2004 2005 2006 2007

No data

Fund

ing b

y sou

rce (

$m)

0

2

4

6

8

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007


Pacific Ocean

Indo

nesi

a

Papua New Guinea




All age groups 6 202< 5 years 898 14

5 years 5 304 86




Major Anopheles species: farauti, koliensis, punctulatus, subpictusPlasmodium species: falciparum, vivax




Fever suspected of being malaria All ages 5 157 000 2 036 000 9 407 000 832 328 1 517< 5 years — — —

Malaria cases All ages 1 508 000 628 000 2 540 000 243 101 410< 5 years — — —

Malaria deaths All ages 2 800 1 200 4 800 0.45 0.19 0.77< 5 years 1 200 510 2 100 1.3 0.57 2.3








Reported malaria 619 678 559 644 731 668deaths, all ages




No data 0 0–1 1–100 > 100

Papua New Guinea had an estimated 1.5 million cases and almost three thousand deaths in 2006. Malaria is highly endemic and comparatively stable incoastal areas; it is less stable in the Highlands region, which is prone to epidemics with many fatalities. 70–80% of infections are due to P. falciparum.Malaria is among the leading causes of hospital admission, and among the most important causes of death in children. There was no evidence of asystematic decline in cases and deaths over the period 2001–2006 but data from districts where ITN were distributed show a marked drop in reportedmalaria cases.First-line drugs have been widely available but parasite resistance to the drugs was high. Prior to 2003, investment in malaria controlwas limited. With support from the Global Fund from 2004, ITN have been delivered to about 24% of the population.

Mala

ria ca

ses p

er 1

000

0

50

100

150

200

250

300

350

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.1

0.2

0.3

2001 2002 2003 2004 2005 2006 2007









Indoor residual spraying (IRS) DDT is used for IRS (public health) only Yes 2000 DDT is used alternately with other Yes 2000insecticides in the same area

Insecticide-resistance management No — IRS is the primary vector-control intervention Yes 2000implemented












Treatment of severe malaria AS/AM+AL 2008




Reported malariaadmissions (< 5)



Examined 254 266 228 665 207 901 222 904 267 123 223 464

Positive 89 819 80 903 70 226 83 799 98 762 81 303

P. falciparum 74 117 58 188 55 362 62 918 62 806 56 917

PAPU

AN

EWGU

INEA

Adm

itted

case

s per

100

0

0

1

2

3

4

5

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

120

140

2001 2002 2003 2004 2005 2006 2007



PAPU

AN

EWGU

INEAIII. IMPLEMENTING MALARIA CONTROL



Reported cases NHIS Surveillance data Insecticide-treated nets (ITN) No surveys

Operational coverage of ITNs, IRS and access to medicines NHIS Programme report Treatment No surveys

Financial data NHIS Programme report Use of health services DHS 1996





No. of first-line 3 719 444 9 407 778 1 729 167 7 958 122 3 896 627 4 822 368treatmentcourses received

No. of ACT treatment 89 545 62 620 362 071 321 296 395 185courses received

Others

Bilaterals

European Union

GFATM 2185723 3256526 372986

World Bank

UN agencies 45203


Others

Treatment 8294 47102

Diagnosis 0 16300

IRS

ITN 1207165 2504153


Perc

enta

ge

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

No data

Perc

enta

ge

0

50

100

150

200

250

2001 2002 2003 2004 2005 2006 2007

Operational coverage of first-linetreatment (relative to estimated fevercases in need of treatment)Operational coverage of ACT (relativeto estimated fever cases in need oftreatment)

Fund

ing b

y sou

rce (

$m)

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

1

2

3

2001 2002 2003 2004 2005 2006 2007


Mali

MauritaniaAtlanticOcean

Senegal





5 years 10 159 84




Major Anopheles species: arabiensis, brochieri, coustani, flavicosta, funestus, gambiae,hancocki, melas, nili, pharoensis




Fever suspected of being malaria All ages 4 472 000 1 455 000 8 765 000 370 121 7265 years 1 202 000 391 000 2 356 000 628 204 1 232

Malaria cases All ages 1 456 000 508 000 2 719 000 121 42 225< 5 years 391 000 136 000 731 000 204 71 382





Reported malaria 931 682 960 478 1 414 383 1 195 402 1 346 158 1 555 310 1 170 234cases, all ages


All-cause outpatientv 2 608 245 2 878 312 3 671 650 3 744 390 4 064 305 4 632 716 5 260 160consultations, all ages

All-cause outpatient 712 816 813 345 968 408 985 149 1 059 420 1 191 498 1 380 054consultations, < 5 years

Mala

ria ca

ses p

er 1

000

0

50

100

150

200

250

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.2

0.4

0.6

0.8

2001 2002 2003 2004 2005 2006 2007


No data 0 0–1 1–100 > 100






Senegal accounts for an estimated 1% of all cases in the WHO African Region. Malaria is endemic throughout the country and transmission occursseasonally from June to November. Almost all cases are P. falciparum, but most are unconfirmed. Survey data show that the percentage of householdswith any net or an ITN had increased to 57% and 36%, respectively, by 2006. Over 270 000 households were sprayed in 2007 protecting nearly 700 000people at risk. The NMP delivered about one million courses of ACT in 2006 and 2007. There is no evidence, from routine surveillance, that malariacases and deaths are falling. Funding has increased from about US$ 6 million in 2001 to over US$ 15 million in 2006, financed by the government, theGlobal Fund and other agencies.












Intermittent preventive IPT used to prevent malaria during Yes 2004 IPT implemented countrywide Yes —treatment (IPT) pregnancy
















Examined

Positive 14 261 15 261 28 272 23 171 38 746 49 366 22 638

P. falciparum 14 261 15 261 28 272 23 171 38 746 49 366

SEN

EGA

L

Adm

itted

case

s per

100

0

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

% of cases confirmed% of cases with P. falciparum infection


SEN

EGA

L III. IMPLEMENTING MALARIA CONTROLCoverage of ITN: survey data Coverage of IRS and ITN: programme data


Reported cases PNLP/Base RBM-M&E Surveillance data Insecticide-treated nets (ITN) MICS 2000, DHS 2005, MIS 2006

Operational coverage of ITNs, IRS and access to medicines Idem, PNLP Programme report Treatment MICS 2000, DHS 2005, MIS 2006






Source: DHS 2005, MIS 2006.



No. of first-line 931 682 960 478 1 414 383 1 195 402 1 346 158 1 555 310 990 141treatmentcourses received

No. of ACT treatment 1 036 872 990 141courses received

Others 2 980 300 1 566 940 2 411 500 4 949 358 3 997 426 340 3767

Bilaterals 1 000 000

European Union

GFATM 0 0 1 428 571 2 857 143 10 634 063 8 958 051 1 067 834

World Bank

UN agencies 212 005 444 315 389 208 221 900 426 718 139 532 257 078




Source: DHS 2003, MIS 2006.


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


Others 2 488 318 4 841 952

Treatment 3 273 462 0

Diagnosis 85 000 389 000

IRS 0 0

ITN 1 730 690 4 960 676

Fund

ing b

y sou

rce (

$m)

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Red Sea

Saudi Arabia

Ethiopia

Central African Republic

Chad

Sudan




All age groups 37 707 483< 5 years 5 483 251

5 years 32 224 85




Major Anopheles species: arabiensisPlasmodium species: falciparum, vivax





Malaria cases All ages 5 023 000 3 214 000 7 394 000 133 85 196< 5 years 1 292 000 827 000 1 902 000 236 151 347






Reported malaria 927 397 1 202 822 994 205 693 476 745 606 418 441cases, < 5 years







No data 0 0–1 1–100 > 100

Sudan had about 2.5 million malaria cases in 2006, 62% of the burden in the WHO Eastern Mediterranean Region. Transmission occurs all year roundin the south, and is more seasonal in the northern states. Over 12 million internally-displaced people are at greatest risk. The reported numbers ofcases and deaths were falling between 2001 and 2006, but it is not known whether this reflects a real decline in malaria burden. Khartoum and Geziralaunched a Malaria Free Initiative in 2002. IRS is carried out annually but locally, protecting about 3.2 million people in 2007. The NMCP distributed3 million ITN during 2005–2007 (2 146 088 LLIN). About 4.38 million ACT were delivered in 2006 covering 90% of estimated treatment needs. Fundingexceeded US$ 22 million in 2006, provided by government, UN agencies, the Global Fund and bilateral donors.

Mala

ria ca

ses p

er 1

000

0

100

200

300

400

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

0.05

0.1

0.15

0.2

0.25

0.3

2001 2002 2003 2004 2005 2006 2007





Insecticide-treated nets (ITN) Distribution of LLINS – Free Yes 2001 Distribution – Antenatal care Yes —













First-line treatment of P. falciparum AS+SP, AS+AQ 2004(unconfirmed)

First-line treatment of P. falciparum AS+SP, AS+AQ 2004(confirmed)

Treatment failure of P. falciparum AL, QN 2004

Treatment of severe malaria QN/AM/AS+SP 2004







Examined 1 176 744 1 859 235 1 815 681 3 014 744 3 416 404 3 301 676

Positive 323 402 393 606 850 953 668 484 761 034 721 233 651 091

P. falciparum


SUDA

NAd

mitt

ed ca

ses p

er 1

000

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007



SUDA

NIII. IMPLEMENTING MALARIA CONTROLCoverage of ITN: survey data Coverage of IRS and ITN: programme data


Reported cases NMCP Surveillance data Insecticide-treated nets (ITN) MICS 2000

Operational coverage of ITNs, IRS and access to medicines NMCP Programme report Treatment MICS 2000

Financial data NMCP Programme report Use of health services MICS 2006







Others 607 692 2 938 309 315 722

Bilaterals 0 765 000 7 399 410 1 468 893

European Union 0 732 830 39 900

GFATM 0 12 903 414 13 692 691 4 903 414

World Bank 0 0 11 000 000 11 000 000

UN agencies 1 219 231 1 074 662 2 369 200 500 000

Gov. malaria 861 538 4 278 554 12 185 187 11 800 000expenditure

No data


Perc

enta

ge

0

5

10

15

20

25

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007



Others

Treatment 500 000

Diagnosis

IRS

ITN 9 500 000

Expe

nditu

re b

y lin

e ite

m ($

m)

0

5

10

15

2001 2002 2003 2004 2005 2006 2007


Kyrgyzstan

China

Afghanistan

Uzbekistan

Pakistan

Tajikistan




All age groups 6 640< 5 years 858 13

5 years 5 782 87


High transmission 1/1000 190 3Low transmission (0–1/1000) 4 863 73Malaria-free (0 cases) 1 587 24Rural population 5 011 75


Major Anopheles species: hyrcanus, maculipennis, martinius, superpictusPlasmodium species: falciparum, vivax



Fever suspected of being malaria All ages 310 000 247 000 413 000 47 37 62< 5 years 61 000 48 000 81 000 71 56 94

Malaria cases All ages 2 400 1 900 3 200 0.36 0.29 0.48< 5 years 460 370 620 0.54 0.43 0.72

Malaria deaths All ages 0 0 0 0 0 0< 5 years 0 0 0 0 0 0

Malaria case-fatality rate (%) All ages 0 — — —< 5 years 0 0 0 — — —











No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

2

4

6

8

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007



A successful malaria eradication campaign was carried out in 1957. Case numbers increased again after 1994 following the disruption of healthservices, armed conflict, population movement across the border with Afghanistan, and the launch of various development projects. Incidence peakedin 1997 and, while cases have since declined, there is a persistent risk of malaria everywhere below 2500 metres. There is transmission of both P. fal-ciparum and P. vivax, but the latter is dominant. The recent resumption of P. falciparum transmission in the Khatlon region is a major concern. IRSin selected localities is the principal method of mosquito control, with limited ITN/LLIN distribution. All malaria cases are treated with a full dose ofchloroquine and primaquine. Malaria control is funded mainly by the government and the Global Fund.





All-cause admissions, 11 387 6 160 5 428 151 911 568all ages

All-cause admissions, 231 90< 5 years

Examined 248 565 244 632 296 123 272 743 216 197 175 894 159 232

Positive 11 387 6 160 5 428 3 588 2 309 1 344 635

P. falciparum 826 509 252 151 81 28 7












Oral artemisinin monotherapies banned Yes — Home management of malaria Yes 2004





First-line treatment of P. falciparum AS+SP/AL 2004(confirmed)





TAJI

KIS

TAN

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

Adm

itted

case

s per

100

0Reported malaria admissions/1000, all agesReported malaria admissions/1000, < 5

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007



TAJI

KIS

TAN III. IMPLEMENTING MALARIA CONTROL



Reported cases MOH Surveillance data Insecticide-treated nets (ITN) MICS 2005

Operational coverage of ITNs, IRS and access to medicines MOH Programme report Treatment MICS 2005

Financial data MOH Programme report Use of health services MICS 2005





Source: MICS 2005.





Others

Bilaterals

European Union 250 000 100 000

GFATM 0 0 0 0 1 425 218 1 346 783 1 346 783

World Bank 0 0 0 0 0 0

UN agencies 612 000 20 000 20 000


% of children< 5 years with fever

% of febrile children< 5 years who soughttreatment in health facility

Source: MICS 2005.


Perc

enta

ge

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

1

2

3

4

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

100

200

300

400

500

600

2001 2002 2003 2004 2005 2006 2007

WHO 2010 target

Operational coverage of first-line treatment (relativeto estimated malaria cases in need of treatment)Operational coverage of ACT (relative to estimatedmalaria cases in need of treatment)

0

0.5

1

1.5

2

Fund

ing b

y sou

rce (

$m)

2001 2002 2003 2004 2005 2006 2007 2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

0.1

0.2

0.3

0.4

0.5

Others

Treatment 19 950 19 950 19 950

Diagnosis 53 000 52 000

IRS 200 000 293 000 293 000 293 000

ITN 55 000 55 000 55 000


Bulgaria Black Sea

Mediterranean SeaSyrian Arab Republic Iraq

Russian Federation

Georgia

Turkey





5 years 67 292 91


High transmission 1/1000 0 0Low transmission (0–1/1000) 6 160 8Malaria-free (0 cases) 67 762 92Rural population 23 827 32


Major Anopheles species: sacharoviPlasmodium species: vivax



Fever suspected of being malaria All ages 1 439 000 1 296 000 1 563 000 19 18 21< 5 years 167 000 150 000 181 000 25 23 27

Malaria cases All ages 1 200 1 100 1 300 0.02 0.02 0.02< 5 years 140 130 150 0.02 0.02 0.02

Malaria deaths All ages 0 0 0 0 0 0< 5 years 0 0 0 0 0 0

Malaria case-fatality rate (%) All ages 0 0 0 — — —< 5 years 0 0 0 — — —



Reported malaria 10 801 10 212 9 209 5 289 2 052 767cases, all ages

Reported malaria 1 243 1 165 1 185 665 224 79cases, < 5 years







No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

0.1

0.2

0.3

0.4

0.5

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007



By 1968 malaria was largely under control, but case numbers increased sharply between 1971 and 1977. Prior to the 1970s, P. falciparum was thedominant parasite; however, following control activities most transmission is P. vivax. P. falciparum infections are acquired outside Turkey. Malaria isendemic in the south-east. In 2006, transmission was reported in 7 provinces, but 84% of cases came from Diyarbakir and Sanliurfa. Cases reportedelsewhere in Turkey originated from these 7 provinces. IRS is the principal method of mosquito control, but coverage of households at risk is low in rela-tion to the 6 million people at risk. Drugs to treat P. vivax infections are widely available. The number of reported cases fell steeply between 2003 and2006, but it is not known how much of the decline is due to prevention and treatment. Malaria control is funded exclusively by the government.





Insecticide-treated nets (ITN) Distribution of LLINS – Free No — Distribution – Antenatal care No —








Case management ACT is free or highly subsidized in public No — Free malaria diagnosis and first-line Yes 2000sectors treatment of malaria





First-line treatment of P. falciparum — —(unconfirmed)

First-line treatment of P. falciparum — —(confirmed)

Treatment failure of P. falciparum — —

Treatment of severe malaria — —





All-cause admissions, 5 290 024 5 508 263 5 736 517 6 440 800 7 011 514 7 764 651all ages


Examined 1 550 521 1 320 010 1 187 814 1 158 673 1 042 509 934 839

Positive 10 812 10 224 9 222 5 302 2 084 796

P. falciparum 11 12 12 13 32 29

TUR

KEY

Adm

itted

case

s per

100

0

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

1

2

3

4

5

2001 2002 2003 2004 2005 2006 2007



TUR

KEYIII. IMPLEMENTING MALARIA CONTROL



Reported cases MoH/MCP Surveillance data Insecticide-treated nets (ITN) No surveys

Operational coverage of ITNs, IRS and access to medicines MoH/MCP Programme report Treatment No surveys

Financial data MoH/MCP Programme report Use of health services DHS 2003




No. of ITNsand/or LLINs

No. of first-line 986 852 1 208 158 1 197 132 419 938 854 004 1 139 577treatmentcourses received


Others

Bilaterals

European Union

GFATM

World Bank

UN agencies 10 000 10 000 10 000 10 000 10 000 15 000





Perc

enta

ge

0

5

10

15

2001 2002 2003 2004 2005 2006 2007

Not applicable

Others 932 338 1 291 441 1 864 827 33 403 393 37 876 240 37 892 128

Treatment 0 76 108 0 5 040 45 015 0

Diagnosis 11 121 52 958 0 8 838 57 446 0

IRS 117 472 228 324 473 973 224 798 241 773 180 293

ITN

Fund

ing b

y sou

rce (

$m)

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

Expe

nditu

re b

y lin

e ite

m ($

m)

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007

Not applicable

2001 2002 2003 2004 2005 2006 2007 2001 2002 2003 2004 2005 2006 2007


Kenya

Democratic Republicof the Congo

Sudan

Uganda





5 years 24 058 80




Major Anopheles species: arabiensis, brochieri, bwambae, coustani, funestus, gambiae,hancocki, hargreavesi, nili, paludis, pharoensis, quadriannulatus






Malaria cases All ages 10 627 000 5 544 000 16 118 000 355 185 539< 5 years 6 353 000 1 189 000 12 130 000 1 088 204 2 077






Reported malaria 3 500 000 3 900 000 4 400 000 4 700 000 5 800 000 2 349 615cases, < 5 years



Reported malaria 77 000 47 000deaths, all ages

Reported malaria 2 631 1 332deaths, < 5 years



No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

500

1000

1500

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

1

2

3

4

5

2002 2003 2004 2005 2006 2007


Uganda had an estimated 10.6 million malaria cases in 2006. Transmission occurs all year round in most parts of the country. Case reports were pre-sented only in round numbers from 2001 to 2005, and do not provide a basis for evaluating incidence trends although programme reports show a fall incases and deaths between 2005 and 2006. IRS began on a limited scale in 2006, protecting 500 000 people at risk. 1.9 million LLIN were distributed in2005 and 2006. A 2006 survey showed that 34% of households owned a mosquito net, but only 10% of children slept under an ITN. Although 15 millionACT courses were reportedly delivered in 2006, survey data indicate that only 3% of febrile children received ACT. Funding for malaria control exceededUS$ 75 million in 2006, supported by government, the Global Fund, UN agencies and other external donors.








Indoor residual spraying (IRS) DDT is used for IRS (public health) only No — DDT is used alternately with other Yes — insecticides in the same area


IRS is used for prevention and control Yes 2001 of epidemics

Where IRS is conducted, other options Yes 2002 are also implemented, e.g. ITN




Parasitological confirmation for all age groups Yes — Prereferral treatment at health-facility level Yes — with quinine im or artesunate suppositories









UGA

NDA

Not applicable Not applicable


UGA

NDA






Source: AIS 2004–05, DHS 2006.


No. of ITNs 540 682 1 365 919and/or LLINs

Others 9 500 000 19 000 000

Bilaterals

European Union

GFATM 40 899 062 47 854 144

World Bank

UN agencies 2 000 000 7 000 000



Reported cases HMIS Surveillance data Insecticide-treated nets (ITN) DHS 2000–01, AIS 2004–05, DHS 2006

Operational coverage of ITNs, IRS and access to medicines NMCP, PMI (IRS) Programme report Treatment DHS 2006

Financial data HMIS Programme report Use of health services DHS 2006





Source: DHS 2006.

No data


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

% of children < 5 years with feverwho took antimalarial drugs% of children < 5 years with feverwho took antimalarial drugssame or next day% of children < 5 years with feverwho took ACT% of children < 5 years with feverwho took ACT same or next day

Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

50

100

150

200

250

2001 2002 2003 2004 2005 2006 2007


Kenya

Zambia

DemocraticRepublicof the Congo

Indian Ocean

United Republic of Tanzania





5 years 32 506 82




Major Anopheles species: arabiensis, coustani, funestus, gambiae, merus, nili, paludis,pharoensis





Fever suspected of being malaria All ages 26 393 000 15 549 000 38 066 000 669 394 965< 5 years 12 508 000 7 369 000 18 040 000 1 799 1 060 2 595








All-cause outpatient 28 056 128 29 482 683 26 881 101 24 194 975consultations, all ages


Reported malaria 838 441 14 943 19 547 18 075 20 782deaths, all ages

Reported malaria 443 238 8 170 11 408 9 057 8 453deaths, < 5 years

All-cause deaths, 45 296 60 174 71 074 55 733all ages

All-cause deaths, 21 634 30 924 30 462 21 211< 5 years

No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

200

400

600

800

1000

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

1

2

3

2001 2002 2003 2004 2005 2006 2007


The United Republic of Tanzania had an estimated 11.5 million malaria cases in 2006. Transmission occurs all year round, with seasonal peaks. The majorityof cases are caused by P. falciparum but most are unconfirmed. 10–12 million cases and 15 000–20 000 deaths were reported annually between 2003 and2006. While nationwide trends are unclear, the numbers of cases and deaths have been significantly reduced in Zanzibar, linked to the provision of LLINsand ACT. On the mainland, the NMCP distributed more than 2 million ITNs annually in 2005, 2006 and 2007, and IRS began in 2007. The NMCP delivered23 million ACT courses in 2007 (and 230 000 in Zanzibar in 2006), which should be sufficient to treat all fever cases. No information on funding since2005 has been provided by the programme, but it is known that expenditure on malaria control has increased markedly with external donor support.



Reported malaria 186 949 292 991 730 509 287 577admissions (all ages)

Reported malaria 112 540 173 991 329 672 168 721admissions (< 5)


All-cause admissions, 723 320 1 193 230 1 213 770 1 217 515< 5 years

Examined 4 296 588 5 528 934 7 993 977 4 136 387

Positive 1 960 909 2 490 446 2 756 421 1 926 711

P. falciparum



Insecticide-treated nets (ITN) Distribution of LLINS – Free No — Distribution – Antenatal care Yes 2004













First-line treatment of P. falciparum AL/AS+AQ 2004(unconfirmed)

First-line treatment of P. falciparum AL/AS+AQ 2004(confirmed)

Treatment failure of P. falciparum QN(7d)/AL 2004




UN

ITED

REP

UBL

IC O

FTA

NZA

NIA

Adm

itted

case

s per

100

0

0

20

40

60

80

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007





Reported cases NMCP, ZMCP Surveillance data Insecticide-treated nets (ITN) DHS 1999, DHS 2004–05

Operational coverage of ITNs, IRS and access to medicines NMCP, ZMCP Programme report Treatment DHS 1999, DHS 2004–05

Financial data NMCP, ZMCP Programme report Use of health services DHS 2004



UN

ITED

REP

UBL

IC O

FTA

NZA

NIA





No. of ITNs 467 668 1 466 181 1 790 647 2 634 414 2 874 043 2 967 148and/or LLINs

No. of first-line 28 726 220 725 476 712 363 585 227 047 23 455 260treatmentcourses received


Others

Bilaterals 1 800 000

European Union

GFATM 19 800 000 90 400 000


UN agencies





No data


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

WHO 2010 target

% of children < 5 years with feverwho took antimalarial drugs% of children < 5 years with feverwho took antimalarial drugs same ornext day% of children < 5 years with feverwho took ACT% of children < 5 years with feverwho took ACT same or next day

Fund

ing b

y sou

rce (

$m)

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

20

40

60

80

100

120

2001 2002 2003 2004 2005 2006 2007



Angola

Malawi

Mozambique

Zimbabwe

Zambia





5 years 9 684 83




Major Anopheles species: arabiensis, funestus, gambiae, nili, pharoensis, quadriannulatusPlasmodium species: falciparum, vivax











Reported malaria 2 295 738 2 230 107 2 480 157 2 324 580 2 360 307 2 434 135cases, < 5 years




Reported malaria 5 498 4 717 4 653 4 008 3 470 3 342 3 783deaths, < 5 years



No data 0 0–1 1–100 > 100

Mala

ria ca

ses p

er 1

000

0

500

1000

1500

2000

2001 2002 2003 2004 2005 2006 2007


Mala

ria d

eath

s per

100

0

0

1

2

3

4

5

2001 2002 2003 2004 2005 2006 2007


Malaria transmission is seasonal, occurring mainly from November to May. The majority of cases are caused by P. falciparum but little confirmationis done. In 2007, reported in-patient malaria cases and deaths in children < 5 years were lower by 33% and 24%, respectively, than the averageof 2001–2003 (prior to interventions). IRS is not the primary preventive strategy, but 2.4 million people at risk were protected in 2006. The NMCPdistributed 1.7 million LLINs in 2005 and 2006. A 2006 survey found that 44% of all households owned an ITN; ITN use in children was 24%. 58% ofchildren with fever received an antimalarial drug in 2006; only 13% received ACT, though access to ACT is increasing. Except government expenditures,little financial information was provided although the programme is increasingly funded by the Global Fund, the World Bank, bilateral organizations andothers.






All-cause admissions, 379 811 424 748 348 864 289 082 300 804 307 443< 5 years






Indoor residual spraying (IRS) DDT is used for IRS (public health) only Yes 2001 DDT is used alternately with other Yes 2001insecticides in the same area











First-line treatment of P. falciparum ALl 2002(confirmed)





ZAM

BIA

Adm

itted

case

s per

100

0

0

50

100

150

2001 2002 2003 2004 2005 2006 2007


No data




Reported cases HMIS Surveillance data Insecticide-treated nets (ITN) MICS 1999, DHS 2001–02, MIS 2006

Operational coverage of ITNs, IRS and access to medicines NMCP Programme report Treatment MICS 1999, DHS 2001–02, MIS 2006

Financial data HMIS Programme report Use of health services DHS 2001



ZAM

BIA



Source: DHS 2001–2002, MIS 2006.

No. of households 69 774 175 192 236 759 537 877 657 695protected by IRS


Others 5 000 000

Bilaterals

European Union

GFATM 4 653 400 11 440 135 946 665 1 400 000


UN agencies 450 000

Gov. malaria 160 000 302 860 588 571 460 380expenditure



Source: DHS 2001–2002, MIS 2006.

No data


Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Perc

enta

ge

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007

WHO 2010 target

Perc

enta

ge

WHO 2010 target

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007


Fund

ing b

y sou

rce (

$m)

0

5

10

15

20

2001 2002 2003 2004 2005 2006 2007

Perc

enta

ge

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007


No. of 1st-line 1 184 698 1 379 955 2 111 348 3 036 982treatmentcourses received

No. of ACT treatment 1 184 698 1 379 955 2 111 348 3 036 982courses received

ANNEX 1

Estimating the numbers of malaria cases and deaths by country in 2006


the working definition of a case of malaria is considered to be “fever with parasites” which normally defines all those that require antimalarial treatment (1).1 estimates of the number of malaria cases and fever suspected of being malaria have been made by one of two methods:

1. By adjusting the reported malaria cases for reporting completeness, the extent of health service utilization and the likelihood that cases are parasite-positive; where data permit, this is generally the preferred meth-od and it was used for countries outside the african region and selected african countries*.

2. From an empirical relationship between measures of malaria transmission risk and case incidence; this pro-cedure was used for countries in the african region where a convincing estimate from reported cases could not be made.

estimates of the number of malaria deaths were also made by one of two methods:

1. By multiplying the estimated number of P. falciparum malaria cases by a fixed case fatality rate for each coun-try. this method was used in countries where malaria accounts for a relatively small proportion of all deaths, and where reasonably robust estimates of case inci-dence could be made. this method was used primarily for countries outside the african region.

2. From an empirical relationship between measures of malaria transmission risk and malaria-specific mortality rates. this procedure was used primarily for countries in the african region where estimates of case incidence could not be made from routinely reported cases.

1. Estimates of cases by adjusting the malaria cases reported by countries

the principal source of data was outpatients attending health facilities as recorded in the health management information system (HMiS) of a ministry of health, or by disease surveillance systems. National malaria control pro-grammes (NMCp) were asked to report on: (i) the number of probable or unconfirmed cases recorded – these are cases that were treated as malaria but not tested or confirmed;2 (ii) the number of cases confirmed by slide examination or rdt; and (iii) the number of slide or rdt examinations undertaken. the relationship between these numbers is summarized in Fig. 1 (2).3

the following steps were used to estimate the number of malaria cases in a country:

1. the total number of parasite-positive cases that attend-ed health facilities covered by the HMiS of a ministry of health was estimated; this is given by the confirmed malaria cases plus the unconfirmed cases multiplied by the slide positivity rate.

2. the revised estimate of confirmed cases was adjusted to take account of missing HMiS reports; by dividing by the health-facility reporting completeness fraction.

3. the estimated number of confirmed cases attending health facilities in the HMiS was then adjusted to take into account the propensity of fever cases to use health facilities not covered by the HMiS (e.g. those going to the private sector), or not to seek treatment at all.

Upper and lower limits for the estimated number of cases, M, arising in any given year in a country are given by:

1 there are, however, widely recognized exceptions to this notion. in much of sub-Saharan africa, antimalarial treatment may be recommended for all fever cases without parasite confirmation, particularly for children under 5 years. in these circumstances it is also useful to estimate the number of fever cases. in other situations, parasites may be detected in individuals that do not have fever. No special provision has been made for such asymptomatic malaria cases even though they would normally be eligible for treatment, since they rarely receive treatment.

2 WHO expert committee on malaria: twentieth report. Geneva, World Health organization, 1998.

3 adapted from (2).

C + (s x U) 1 Mupper = x r u

C + (s x U) 1 – n Mlower = x r u

Where:

C = reported number of confirmed malaria cases in a year.

U = reported number of probable or unconfirmed cases in a year – cases suspected of being malaria but not tested or confirmed.

s = the proportion of slides examined that are positive for malaria parasites, often known as the slide positivity rate (Spr). in countries that are unable to undertake microscopic examination of all suspected cases, s was derived from a selection of facilities, or treatment outlets, that undertake case confirmation. the propor-tion of cases found to be positive, s, was applied to cases that were not tested.

r = Completeness of health-facility reports. typically this is the number of outpatient health-facility reports received divided by the number of facility reports expected. the expected number of reports is given by the number of health facilities multiplied by the number of reports expected to be submitted by each health facility in a year, which is 12 for a monthly reporting system. the quantity r has been reported by countries as lying within three possible ranges (less than 50%, 50–80% and > 80%). in order to calculate an average reporting rate, it has been assumed that the reporting rate has a triangular distribution in the outer ranges and a uniform distribution in the middle with expected values in these ranges of 33%, 65% and 87% respectively.

* Cibulskis re et al. estimating trends in the burden of malaria. American Journal of Tropical Medicine and Hygiene, 2007, 77(suppl 6): 133-137.


u = The proportion of the population with fever (or sus-pected malaria) that uses health facilities that arecovered by the health-facility reporting system. Thiswas derived from household survey information onwhether or not children under 5 years, with fever inthe previous two weeks, sought treatment and where.The household survey used for most countries wasa DHS or MICS that only considers fever in childrenunder 5. Fever treatment rates for older age groups are

Fig. 1 Relationship between suspected, unconfirmed and confirmed malaria

Confirmed malaria(uncomplicated and severe)

Negative(confirmed NOT malaria)

Fever suspected of being malaria(uncomplicated and severe)

Probable or unconfirmed malaria(uncomplicated and severe)

Patients tested by slide or RDT(uncomplicated and severe)

Fig. 2 Variation in utilization of health facilities for treatment offever by agea

a Analyses of household surveys undertaken (a) in India (NSSO, 2004) and (b) in Indonesia (Suse-nas, 2004) suggest that the percentage of children under 5 using health facilities for treatment offever provides a good approximation to the percentage of older age groups using health facilitiesfor treatment of fever.

(a)

(b)

Feve

r cas

es se

ekin

g tre

atm

ent (

%)

0

20

40

60

80

100

0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69

Age group

Age group

0

20

40

60

80

100

Feve

r cas

es se

ekin

g tre

atm

ent (

%)

1–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40+

Government facility Private facility Shop or pharmacy No treatment

Public sector Private sector Informal No treatment

assumed to be the same as those for children under 5.Household surveys undertaken in Indonesia (Susenas)and India (NSSO) suggest that this assumption is rea-sonable (see Fig. 2).

Several of the MICS3 surveys were not available foranalysis at the time of publication of this report, butotherwise the DHS, MICS or other survey closest to2006 was chosen. In general, there has been littlechange in the proportion of fever cases seeking treat-ment over time (see Fig. 3), hence it is a reasonableapproximation to the proportion of fever cases seekingtreatment in 2006, unless there is specific knowledgethat treatment-seeking behaviour has changed.

If there was more than one survey for a country, thefollowing choices were made:

1. if both a DHS and a MICS were available for theperiod 2001–2006, the DHS was selected in prefer-ence to the MICS, even if the DHS was undertakenearlier, since sample sizes were larger and u can becalculated more directly;

2. if a MICS was available for the years 2001–2006 anda DHS only for 2000 or earlier, the MICS was select-ed because it was more recent;

a Of 58 survey differences that could be compared over time using DHS and MICS data sets, themedian annual change in the proportion of febrile children attending health facilities was 0.1%(inter-quartile range: 1.4%–2.0%).

Annual change in percentage of children using health facilities for treatment of fever (%)

Num

ber o

f hou

seho

ld su

rvey

s com

pare

d

0

2

4

6

8

10

12

14

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

Fig. 3 Variation over time in the percentage of fever casesseeking treatment at health facilitiesa


3. if a MICS and a DHS were only available in 2000or earlier, the DHS was selected. In total, relevanthousehold survey data could be found for 69 coun-tries.

For DHS surveys, the measure of u is the proportion offever cases seeking treatment from hospitals, healthcentres and health posts run by the government andmissions but excludes cases treated at private for-profit organizations and pharmacies or shops. It alsoexcludes those cases not seeking any treatment.If more than one source of treatment was used, thesource of treatment considered to be at the highestlevel or furthest along the referral chain was selected,with government facilities ranked higher than missionor private facilities as in the following order: govern-ment hospital, government health centre, governmenthealth post, other government facility, mission hospi-tal, mission health centre, mission health post, privateclinic or health centre, private doctor, private other,community health worker, pharmacy, shop, traditional,other. The ranking was intended to reflect the proba-bility that a case attending multiple treatment outletswould be recorded in the HMIS of a ministry of health,but the precise classification used has little effect onthe final results since the percentage of children usingmore than one outlet for treatment is only 5% (seeFig. 4).

In the majority of MICS surveys, there is a questionthat records the proportion of fever cases seekingtreatment at health facilities but the type of healthfacility attended, and whether it is in the public or pri-vate sector or not, is not distinguished. Hence for MICSsurveys, information on health-facility utilization foracute respiratory infection (ARI) was combined withfever treatment to estimate the proportion of fevercases attending public-sector facilities as below:

Where:

hfever = The proportion of children with fever in thepast two weeks who attended a health facility.

h ARI = The proportion of children with both cough andrapid breathing in the past two weeks who attended ahealth facility.

gARI = The proportion of children with both cough andrapid breathing in the past two weeks who attended agovernment or mission health facility.

For countries with both a DHS and a MICS survey, therewas no systematic deviation in the estimate of u madefrom either the DHS or the MICS (Fig. 5).

For Papua New Guinea and Myanmar the householdsurveys, a DHS and a MICS respectively, did not recordhealth-facility utilization for fever. Hence informationon health-facility utilization for ARI (cough and/or rap-id breathing) was used to estimate u since it is observedfor other countries that health-facility utilization ratesfor ARI serves as a reasonable approximation for health-facility utilization rates for fever (see Fig. 6).

For the Solomon Islands and Guinea-Bissau, confidenceintervals of u could not be calculated because of insuffi-cient information on the sampling procedures employed.Confidence intervals were therefore assumed to lie 7.5percentage points either side of the point estimate ofu, since 90% of measured confidence intervals were ofthis size or smaller in other countries.

n = The proportion of the population with fever (or sus-pected malaria) that does not seek treatment. Thiswas derived from household survey information onwhether or not treatment for fever was sought, asdescribed above.

The difference between upper and lower limits on M reflectsthe extent to which it is believed that malaria cases aretreated in the health system (both formal and informal).The upper limit produces an estimate of the number of

Fig. 4 Percentage of fever cases using more than one outletfor treatmenta

1 2 3 4 5+

Number of treatment outlets used

Child

ren

seek

ing t

reat

men

t for

feve

r (%

)

0

20

40

60

80

100

a In an analysis of 105 DHS surveys (with each survey weighted equally), the proportion of fevercases using more than one treatment outlet was only 5%.

Fig. 5 Percentage of fever cases taken to health facilitiesa

a Comparison of estimates of u (the percentage of fever cases taken to a facility included in acountry’s HMIS) derived from MICS and DHS suggest no systematic deviation in estimatesderived from different survey platforms.

Fever cases taken to facilities included in the HMIS as estimated by MCIS (%)

15 20 25 30 35 40 45 50 55 60

Feve

r cas

es ta

ken

to fa

ciliti

es in

clude

d in

the

HMIS

as e

stim

ated

by D

HS (%

)

0

10

20

30

40

50

60

gARIu = hfever xhARI


malaria cases if all fever cases were equally likely to bemalaria whether they sought treatment or not, i.e. thosethat do not seek treatment have the same slide positivityrate, s, as those that do. The lower limit estimates thenumber of malaria cases if only those fever cases that seektreatment were malaria, i.e. the slide positivity rate, s, offever cases not seeking treatment is zero. In practice thetrue value will probably lie between these points. It willlie close to the lower limit in areas where accessibility toservices is good and all cases that need treatment actuallyseek it. It will lie closer to the upper limit in areas whereaccessibility of services is poor, and many malaria casesgo untreated. It was only possible for a few countries toassume that all cases seek treatment; these were countriesin Europe and some countries nearing malaria elimina-tion (where most infections produce symptomatic casesand where access to services is good). In the absence ofdetailed information on the structure of health services ina country, it is expedient to derive a single point estimate,I from the arithmetic of average of Ilower and Iupper.

Estimating fever cases and the need for treatment. An estimate ofthe total number of fever cases suspected of being malaria,F, was made by dividing the estimated confirmed cases bythe slide positivity rate. Thus the upper and lower limitsof F are given by:

As for confirmed malaria cases, a single point estimateof the number of fever cases, F, can be obtained from thearithmetic of average of Flower and Fupper. The number oftreatment courses required, T, depends on the extent towhich case confirmation is undertaken as below:

T = F x e x s + (F x (1 – e))

Where:

T = The number of treatment courses required per year.

M = The estimated confirmed malaria cases per year.

F = The estimated fever cases per year.

e = The proportion of suspected cases tested.

If all cases are confirmed (e = 1), the number of treatmentcourses required is given by:

T = F x s = M

Which is the estimate of confirmed malaria cases. If nocases are confirmed (e = 0), then:

T = F

Values of C, U, s, r, n, u, M, F, e and T are given for eachcountry at www.who.int/topics/malaria/en. Note that e isestimated from patients attending facilities included inministry of health reporting systems only.

Lack of data. The formulae used to estimate the number ofmalaria cases require six parameters. Four parameters areobtained from the national malaria control programmes (U,C, r, s) and two from household surveys (n and u). Somecountries did not provide values for all parameters or ahousehold survey may not have been available for analy-sis. In such cases, values for the parameters were imputedaccording to the scheme below:

U or C: If U or C was missing from 2006 the value of Uand C from the most recent year was taken (thisapplied to Mexico and Peru only). If no values of Uand C were available then no estimate of malariacases was made.

r: If the reporting completeness, r, was not reportedfor a single year the most frequent value for thecountry between the years 2001 and 2006 waschosen or, exceptionally, a rate that was consid-ered more consistent with the reported number ofcases. If r was missing for all years, it was assumedto lie between 50% and 80% with a point estimateof 65%.

s: If the slide positivity rate, s, was missing for a year,then the country arithmetic average for availableyears between 2001 and 2006 was taken with eachyear being weighted equally. If s was missing forall years, the regional arithmetic average for theyear 2006 was applied with each country weightedequally.

Fig. 6 Percentage of fever cases seeking treatment for fever(at all outlets) plotted against the percentage of ARI casesseeking treatmenta

a Analysis of 105 demographic and health surveys suggests that the percentage of children withARI that seek treatment can provide a good approximation to the percentage of children withfever that seek treatment.

Children seeking care for ARI (%)

Child

ren

seek

ing c

are f

or fe

ver (

%)

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100

Mupper C + (s x U) 1Fupper = = x

s r x s u

Mlower C + (s x U) 1–nFlower = = x

s r x s u


u, n: If u and n were missing, household surveys fromother countries in the region were used to deriveregional averages of u and n with each countryweighted equally.

Uncertainty analysis. An attempt was made to quantify theuncertainty in each of the parameters and to use thisinformation to construct a plausible range within which itis reasonably certain that the estimate of the number offever and malaria cases lies. The underlying distributionassumed for each of the parameters is shown in Table 1.Pallisade @Risk software (version 5.0) was used to samplefrom the distributions assumed for each parameter and eachcountry. Latin Hypercube sampling was performed 1000times to yield a distribution for the estimated number offever and malaria cases for a country, from which the meanvalue was taken together with 5% and 95% uncertaintylimits for the mean.

Limitations. Not all aspects of the uncertainty were mod-elled. Moreover, the assumptions regarding the distribu-tion of some parameters may not always apply. Particularconcerns regarding different parameters are summarized inTable 2. Recognition of the limitations, along with infor-mation on how data are collected in a country, suggestssteps that may be taken to improve estimates in particu-lar countries. An index of the quality of data availablein each country has been constructed taking into accountthe completeness of data in the HMIS of a ministry ofhealth and the availability of household survey informa-tion. It ranges from zero (no data) to 10 (maximum score).Values for each country are are available from www.who.int/topics/malaria/en and summarized regionally in Fig.7. Where data systems are good and household surveysrecent, greater confidence may generally be placed in anestimate.

Fig. 7 Range of data quality scores achieved by countries withineach WHO regiona

a The data quality score takes into account the completeness of data in the HMIS of a ministry ofhealth and the availability of household survey information. It ranges from zero (no data) to 10(maximum score). The highest scores are obtained in the WHO region where the malaria burden islowest and the lowest scores are obtained in the region where the burden is highest.

Region

Tota

l

0

2

AFRO AMR EMRO EURO SEARO WPRO

4

6

8

10

Table 1 Distributions assumed for parameters inuncertainty analysis

ASSUMED DISTRIBUTION IF PARAMETER DERIVED FROM REPORTED DATA

r For each reported value of reporting completeness, r wasassumed to be distributed as follows:

REPORTED DISTRIBUTION MINIMUM MOST MAXIMUMVALUE LIKELY

80% + Triangular 80% 80% 100%

50–80% Uniform 50% 80%

< 50% Triangular 0% 50% 50%

s The uncertainty analysis aimed to reflect the variation of swithin a country, so that when s was applied to cases thatwere not microscopically examined the slide positivity ratecould take on a range of values that could reasonably beexpected to occur across the country. Specifically, the nationalslide positivity rate, s, was assumed to be distributed normallywith a mean c and standard deviation of 0.311s0.5547. Valuesof s were then truncated so that values lie between 0 and 1.This relationship was obtained from a least squares regres-sion of the mean value of s against the standard deviationof s for each country for which subnational values of s wereavailable.

u and n u and n were assumed to be distributed normally with meanand standard deviation derived directly from analysis ofhousehold surveys, taking into account the specified samplingdesign.

ASSUMED DISTRIBUTION IF PARAMETER IMPUTED

r The reporting rate was assumed to have uniform distributionwith a range between 50% and 80%.

s If a country did not report a slide positivity rate, values of sfrom other countries in the relevant WHO region were appliedand assumed to occur with equal probability.

u and n If a relevant household survey was not available for a country,values of u and n from other countries in the relevant WHOregion were applied and assumed to occur with equal prob-ability.


Table 2 Potential problems and consequences of uncertainty in parameters used to estimate malaria cases

POTENTIAL PROBLEM CONSEQUENCE

REPORTING COMPLETENESSCountries may not keep a complete and up-to-date list of all open health Reporting completeness overestimated and malaria burdenfacilities, and reporting completeness may have been provided only for underestimated.those facilities that are known to malaria control programmes.

If health facility reports are aggregated at a district level then, in the Reporting completeness overestimated and malaria burdenabsence of other information, when a district report is received it may be underestimated.assumed that all health facilities in the district have reported. Similarly if reports are aggregated quarterly they may contain incomplete monthly information but be counted as complete. If accurate monitoring of the percentage of reports received is not kept, then reporting completeness may be overestimated.

The analysis undertaken does not consider the type of institution failing If hospitals are more likely to underreport, the reporting completenessto report. Failure of a hospital to report will generally have a greater will be overestimated. If health posts are more likely to underreport, influence on the reported number of malaria cases than a health post. In reporting completeness will be underestimated. some countries malaria programmes have difficulty obtaining data from hospitals that use a separate reporting system. In other countries, missing reports may be mostly those from health posts and reporting completeness underestimated.

UTILIZATION OF PUBLIC HEALTH FACILITIESDHS and MICS were used to estimate the proportion of malaria cases There is no comprehensive evidence to suggest that other age groupsattending public health facilities, private health facilities, pharmacies or use health services more or less than children under 5 years in responseshops and those not seeking treatment at all. These proportions were to reported fever. Potential consequence unknown.derived from children under 5 who experienced fever in the two weeks before the survey. Care-seeking behaviour in children under 5 seemed to provide a reasonable approximation to care-seeking behaviour in other age groups in two countries where it could be checked, but may not apply elsewhere.

Care-seeking behaviour for self-reported fever may not necessarily There is no comprehensive evidence that fever differs significantly fromreflect care-seeking behaviour for suspected or confirmed malaria. true malaria. Potential consequence unknown.

Only 9 of the 69 household surveys analysed were conducted in 2006. There is no evidence that the percentage of fever cases using85% of surveys were from 2000 or later, with the median age of survey government health services has either increased or decreased. Potentialbeing 5 years. Utilization of health services may therefore be under- consequence unknown.or overestimated

A single national estimate of the proportion of fever cases attending Potential overestimation of the proportion of malaria cases attendingpublic health facilities was used. In some countries, the availability and public health facilities. Simultaneously the proportion of malaria casesaccessibility of services may be greater in areas with less malaria. using private health facilities may be overestimated. The combinedConversely services may be less accessible in areas where there is effect of these tendencies is unknown.more malaria.

The uncertainty analysis considered only sampling variation in the Potential underestimation of the uncertainty regarding case estimates.estimation of u and n. The potential effect of misclassification of treatment outlets as being covered by the HMIS or not was not explored.

SLIDE POSITIVITY RATEHealth facilities that undertake slide examination may only do so for If slide examination is reserved for more severe cases, the number ofselected patients, e.g. those admitted, or for adults. confirmed malaria cases may be overestimated. If slide examination is

reserved for adults, the number of confirmed malaria cases may be underestimated. The combined effect of these tendencies is unknown.

A slide positivity rate derived from selected government facilities is If facilities undertaking slide examination are located in more developedapplied to suspected malaria cases attending other facilities to estimate or urban areas, the true proportion of suspected cases that areconfirmed malaria cases. Health facilities not undertaking case confirmed may be underestimated. If slide examination is more likelyconfirmation may differ qualitatively from those undertaking slide to be undertaken in areas where malaria transmission is more intense, examination (e.g. they may be in different parts of the country) and the proportion of all cases that are confirmed will be overestimated. Theobtain a different SPR. combined effect of these tendencies is unknown.

An SPR from public health facilities is applied to private facilities No evidence that slide positivity in the private sector differs from that inincluding shops and pharmacies, but the true rate may be different. the public sector. Potential consequence unknown.

On average an SPR of half of that found in public health facilities is Knowledge of infection rates in fever cases that do not seek treatment isapplied to fever cases not attending facilities; the range of SPR used insufficient. Potential consequence unknown.being from 0 to s.


2. Estimates of malaria cases from an empirical relationship between measures of malaria transmission risk and case incidence

The procedure follows that of Snow et al. (3) and Koren-romp et al. (1). The number of malaria cases was estimated in two steps: firstly, populations in each sub-Saharan coun-try were classified as living at either high, low or no risk of malaria; secondly, high, low or zero case-incidence rates were applied to the number of people living in each ende-micity class as derived from literature reviews. The case-incidence rates were subsequently adjusted to take into account the level of malaria intervention coverage (use of ITNs and IRS only). Such an approach was attempted only (i) for countries in the WHO African Region where it is assumed that transmission is relatively homogenous and a broad categorization of malaria risk into low-transmission and high transmission is possible; and (ii) for countries with profound deficiencies in malaria reporting.

Defining malaria risk. Malaria risk for countries in the African Region was defined according to climatic suitability, as per the Mapping Malaria Risk in Africa (MARA) project estimate for the year 2002.1 Long-term climate data were used to define the probability of malaria transmission, in terms of a “climate suitability index”, ranging from 0 (unsuitable) to 1 (very suitable), at a resolution of 5 × 5 km2. Areas with a MARA index greater than 0.75 were considered to be at high transmission risk, and areas with a MARA index below 0.75 at low transmission risk (5). These two categories are thought to correspond broadly with parasite prevalence in childhood populations greater than or equal to 25%, and less than 25% respectively (6). The proportion of a coun-try’s 2002 population reported to be living at high, low and no risk (7) was applied to the 2006 country populations as projected by the UN Population Division (8).

Deriving case-incidence rates. “Basic malaria incidence rates” were derived for populations at high and low transmis-sion risk from a review of longitudinal studies of popu-lations not benefiting from malaria prevention (3). More than 20 studies were from high-intensity transmission areas (defined as parasite prevalence 25% or MARA cli-mate suitability index 0.75), and four from low-intensity transmission areas (parasite prevalence < 25% and MARAclimate suitability index between 0 and 0.75). The rates for southern African countries were derived from national surveillance data from Botswana, Namibia, South Africa, Swaziland and Zimbabwe, and were not thought to vary by age. The number of studies and median incidence rates with inter-quartile ranges (IQR) for each category of trans-mission risk are shown in Table 3.

Adjusting for urban–rural differences. Adjustments to basic incidence rates were made for urban settings following

Korenromp et al. (1). Urban incidence rates for children under 5 were assumed to be half those of rural areas, as determined (approximately) from other community-based studies of urban–rural differences (9). Incidence rates for other age groups in high-transmission areas were consid-ered to be the same as for rural areas. Incidence rates for urban low-transmission areas were considered to be the same as rural low-transmission areas, although Carneiro et al. (9) note one study in Kenya which estimated urban incidence rates to be 40% of those in rural areas for chil-dren under 5. Similarly, Korenromp et al. note that, outside Africa, urban incidence rates were approximately one third of those in rural areas. Hence the assumption that urban incidence rates in low-transmission areas are the same as rural incidence rates may lead to an overestimate of malar-ia incidence in urban low-transmission areas. However, the influence on individual country estimates is relatively minor as less than 25% of any country is considered to be urban and low-transmission, while for sub-Saharan Africa as a whole it is about 6.5%.

Adjusting for malaria preventive activities. Since the basic inci-dence estimates were for populations not subject to malar-ia preventive measures, they are adjusted downward for each country according to the expected impact of ITNs and IRS. Local impact was calculated based on an assumed fixed efficacy of 50% reduction in incident cases for both IRS and ITNs, at an ITN usage rate of 60% among chil-dren under 5, according to observations in randomized controlled trials (10). Local impact was then calculated by applying the 50% efficacy to the local ITN child usage rate derived from national household surveys as listed in Annex 6, assuming a linear decrease in efficacy with cov-erage lower than 60%. The last two steps, adjusting for urban–rural differences and impact of preventive activi-ties, were not undertaken by Snow et al., but were fol-lowed by Korenromp et al. They result in a reduction in the number of cases estimated for the continent of 9% and 5% respectively, or 14% combined. The calculated impact of ITNs and IRS appears quite small and this is partly because some of the household surveys documenting ITN use were undertaken several years before 2006 and may not reflect current levels of use.

Uncertainty analysis. An attempt was made to quantify the uncertainty in the malaria incidence rate that could be applied to each category of malaria transmission. A trun-cated triangular distribution of basic incidence rates was assumed for each age group and category of transmission risk. The median and inter-quartile ranges of the triangular distributions were the same as those of the basic incidence rates shown in Table 3. The triangular distributions were truncated so that their lower limit did not fall below 1. Inrepeated simulations the truncated triangular distribution was found to provide a good approximation for the distri-bution of basic incidence rates inferred from individual studies. Its use was preferred to deriving a distribution of basic incidence rates from a separate literature review

1 See www.mara.org.za. More recent work on defining populations at risk (4) does not provide such a detailed classification of risk with 97.5% of the population at risk within Africa being classified as stable and 2.5% unstable.


in order to make the results more consistent with those of Snow et al.

Palisade @Risk software (version 5.0) was used to sam-ple from the distributions assumed for each parameter. Lat-in Hypercube sampling was performed 1000 times for each country to yield a distribution for the estimated number of fever and malaria cases for a country, and for the African continent, from which the mean value was taken together with 5% and 95% uncertainty limits for the mean.

Limitations. The methods used to estimate malaria case inci-dence from malaria transmission risk provide only a very rough guide to the number of malaria cases in any country because:

1. The delimitation of only two risk categories (high and low) does not provide for a fine categorization of malar-ia risk; a particular risk category may contain a wide range of malaria incidence and death rates.

2. The model to determine the suitability of the climate model for malaria transmission was based on a 30-year average of climatic variables. There is known to be variation year by year in the suitability of climate for malaria transmission, and this annual variation was not taken into account in the uncertainty analysis, nor was the suitability of the climate for malaria transmission in 2006 specifically estimated.

3. The studies used to derive basic incidence rates were not designed to be representative of the levels of ende-micity they purport to describe, are small in number, and show a wide variation in measured case incidence with few, if any, studies in urban areas and low-risk rural areas which required rates to be inferred.

4. The studies used to derive basic incidence rates within categories of endemicity, urbanicity and age group were mostly conducted during the 1990s and before, and do not necessarily reflect rates in 2006. Notably, the influence of artemisinin-based treatments on reducing transmission and case rates was not captured.

5. The adjustments made to take into account the effects of interventions on case incidence are based on a rela-tively small number of clinical trials, run for only two

years after the introduction of ITNs, which tended to show higher levels of intervention coverage than observed in most countries. Moreover the assumption of efficacy varying linearly from lower to higher coverage levels was not based on empirical evidence.

3. Estimates of malaria deaths by applying a fixed case fatality rate to the estimated number of P. falciparum malaria cases for each country

This method was used for all countries outside the African Region and to countries in the African Region where esti-mates of case incidence were derived from routine report-ing systems and where malaria comprises less than 5% of all deaths in children under 5 as described in the Global burden of disease: 2004 update (GBD 2004) (11) . The Afri-can countries were Botswana, Namibia, South Africa and Swaziland, each of which had less than 5% of all deaths of children under 5 years attributed to malaria in GBD 2004, and Eritrea where recent trends in case incidence suggest that malaria accounts for less than 5% of deaths. In such situations, estimates of case incidence were considered to provide a better guide to the levels of malaria mortality than attempts to estimate the fraction of deaths due to malaria when the fraction of malaria deaths is small.

An estimate of P. falciparum cases in each country was made by multiplying the total number of estimated cases by the percentage of cases that were reported to be P. fal-ciparum by national malaria control programs for the World malaria report 2008. If the information on the percentage of malaria cases due to P. falciparum was missing for a country then it was taken from GBD 2004. A case fatal-ity rate of 0.45% was applied to the estimated number of P. falciparum cases for countries in the African Region and a case fatality rate of 0.3% for P. falciparum cases in other regions. Case fatality rates from malaria in populations are not well documented. Mendis et al (12) estimated P.falci-parum case fatality rates to lie within a range of 0.01 to 0.3% outside of Africa. Hence, the value used in the World malaria report 2008 corresponds to the upper limit of this range.

For most countries the resultant estimates of malar-ia specific mortality fitted into the permitted mortal-ity envelops described in GBD 2004. However, for Papua

Table 3 Malaria case-incidence rates by transmission risk category (cases per person per year)

HIGH TRANSMISSION LOW TRANSMISSION SOUTHERN AFRICA

n Median IQR n Median IQR n Median IQR

Under-5s 28 1.424 0.838 – 2.167 4 0.182 0.125 – 0.216 5 0.029 0.097 – 0.1295–14y 19 0.587 0.383 – 0.977 0.182a 0.125 – 0.216 0.029 0.097 – 0.129>=15y 7 0.107 0.074 – 0.138 0.091b 0.063 – 0.108 0.029 0.097 – 0.129

UrbanUnder-5s 0.712c 0.419 – 1.084 0.182d 0.125 – 0.216 0.029 0.097 – 0.1295–14y 0.587d 0.383 – 0.977 0.182d 0.125 – 0.216 0.029 0.097 – 0.129>=15y 0.107d 0.074 – 0.138 0.091d 0.063 – 0.108 0.029 0.097 – 0.129

a No observations available so assumed to be the same as that measured in children under 5 by Snow et al. (3).b No observations available so assumed to be half the rate of children 5–14 years by Snow et al. (3).c Estimated to be approximately half the rate of rural areas by Korenromp et al. (1) and Carneiro et al. (9).d Considered to be the same as in rural area by Korenromp et al. (1).


New Guinea, Solomon Islands and Vanuatu the estimatednumber of deaths were scaled downwards to ensure thatthe number of malaria deaths does not require an upwardadjustment of all-cause mortality rates estimated for thecountry. The resultant P. falciparum case fatality rates forthe three Pacific nations were 0.17%, 0.26%, 0.26% and0.19% respectively. The number of malaria deaths in chil-dren under 5 was estimated by multiplying the estimatednumber of malaria deaths in all age groups by the percent-age of deaths estimated to occur in children under 5 yearsfor each country in GBD 2004.

Uncertainty analysis. Uncertainty limits for the number ofdeaths were calculated by assuming a uniform distributionof case fatality rates ranging from 0.225% to 0.675% forAfrican countries and 0.15% to 0.45% outside of Africa.The number of P. falciparum malaria cases for each countrywas assumed to have a uniform distribution with lower andupper limits given by the 5% and 95% uncertainty limitscalculated in the estimation of total number of cases mul-tiplied by the percentage of cases estimated to be P. fal-ciparum in each country. Palisade @Risk software (version5.0) was used to sample from the distributions assumed foreach parameter. Latin Hypercube sampling was performed1000 times for each country to yield a distribution for theestimated number of deaths for a country from which themean value was taken together with 5% and 95% uncer-tainty limits for the mean.

Limitations. The methods used to estimate malaria mortal-ity from malaria transmission risk suffer similar limitationsas the procedure described to estimate malaria case inci-dence from routine case reports. In addition it should benoted that the case fatality rates assumed for differentcountries were assigned without regard to the availabilityand utilization of treatment for malaria, and in practicecould vary from the rate used.

4. Estimates of malaria deaths from an empiricalrelationship between measures of malaria trans-mission risk and malaria-specific mortality rates.

For countries in which an estimate of malaria deaths couldnot be inferred from estimates of case incidence the numberof malaria deaths were derived from the Global burden ofdisease incremental revision for 2004 (from GBD 2004) (11).This includes all countries in the African Region exceptBotswana, Eritrea, Namibia, South Africa and Swaziland.For GBD2004 the malaria mortality estimates for childrenunder 5 years in Africa were based on the work of Roweet al. (13) which summarized studies that were able toestimate malaria-specific death rates in areas with highand low risk of malaria (those with a MARA transmissionsuitability index greater than or equal to 0.75, and thosewith a suitability index greater than 0 but less than 0.75).Malaria death rates were applied to the populations atrisk in each country to obtain the number of deaths frommalaria and the proportion of a country’s expected child-hood deaths due to malaria. The proportions of deaths due

to malaria and other conditions were adjusted to fit post-neonatal child death envelopes for 2004, country by coun-try, as described in Section B2 of GBD 2004. This resultsin a reduction of malaria deaths from those initially esti-mated by Rowe et al (13) of about 10%.

Deaths above 5 years were inferred from a relationshipbetween levels of malaria mortality in different age groupsand the intensity of malaria transmission (14) (Fig. 8). Theestimated malaria mortality rate in children under 5 yearsfor a country was used to determine malaria transmissionintensity and the corresponding malaria-specific mortalityrates in older age groups. The malaria-specific mortalityrates were compared with malaria incidence rates derivedfrom a model similar to that developed by Korenromp etal. (1).1 Implied case-fatality rates were calculated andadjustments made either to the number of cases or deathsin some countries to ensure plausible implied case-fatalityrates. This mainly affected smaller countries and did nothave a significant impact on regional totals.

Malaria-specific mortality fractions for 2004 wereapplied to the mortality envelopes implied by WHO lifetables for 2006 and UN Population Division estimates for2006, in order to obtain the number of malaria deaths bycountry for 2006. In practice the adjustments lead to smallchanges, but they were made in any case to base all esti-mates on a single year.

Limitations. The methods used to estimate malaria mortalityfrom malaria transmission risk suffer similar limitations asthe procedure described to estimate malaria case incidencefrom transmission risk. In addition it should be noted:

1 The principal difference being that if the modelled estimate was morethan 100 times the number of officially reported cases, the estimatewould be replaced by the reported cases multiplied by 50 (rather than100 in the original model) and that all case incidence estimates werereduced by 10% to allow for additional effects of control and develop-ment programmes. These adjustments resulted in a lower number ofestimated cases for all countries, but particularly for larger countriesoutside Africa such as India.

a Predictions of malaria-specific mortality rates from a model developed by Ross et al. (14) usingas input the seasonal pattern of inoculations for Namawala, United Republic of Tanzania, scaledto different entomological inoculation rates (number of infectious bites per person per year).

Fig. 8 Predicted direct malaria mortality rates by transmissionintensitya

Deat

hs p

er 1

000

0

5

10

15

20

25

0

5

10

15

20

25

Entomological inoculation rate (EIR)

1 10 100 1000

Age 0Age 1–4Age 5–19Age 20–39


1. The relationship between malaria mortality rates of children under 5 years and in other age groups versus malaria transmission was derived from a small number of studies and may not be generally applicable.

2. Malaria mortality rates were calibrated with malaria incidence rates derived from a model of incidence based on transmission risk, to ensure that case-fatality rates were plausible. Malaria mortality rates will be prone to error in countries where the model of incidence does not provide reliable estimates.

References1. Korenromp E. Malaria incidence estimates at country level

for the year 2004. Geneva, World Health Organization, 2005 (draft) (www.malariaconsortium.org/resources.php?action= download&id=177).

2. Graves PM. Assessment of the implementation of the WHO Western Pacific Region malaria “Kunming” indicator frame-work, 1999–2005. Consultant report. Manila, WHO Regional Office for the Western Pacific, 2006.

3. Snow RW et al. The public health burden of Plasmodium fal-ciparum malaria in Africa: deriving the numbers. Bethesda, M.D., Fogarty International Center/National Institutes of Health, 2003 (The Disease Control Priorities Project (DCPP)Working Paper Series No. 11).

4. Guerra CA et al. The limits and intensity of Plasmodium fal-ciparum transmission: implications for malaria control and elimination worldwide. PLoS Medicine, 2008, 5(2):e38.

5. Craig MH, Snow RW, le Sueur D. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasi-tology Today, 1999, 15:105–111.

6. Omumbo JA et al. The relationship between the Plasmodium falciparum parasite ratio in childhood and climate estimates of malaria transmission in Kenya. Malaria Journal, 2004, 3:17.

7. Data are available at www.who.int/topics/malaria/en/.

8. United Nations Population Division. World population pros-pects. 2006 revision. New York, United Nations, 2006.

9. Carneiro IA, Roca-Feltrer A, Armstrong-Schellenberg JR.Estimates of the burden of malaria morbidity in Africa in chil-dren under the age of five years. London, London School of Hygiene and Tropical Medicine, 2005 (Child Health Epidemi-ology Reference Group working paper).

10. Lengeler C. Insecticide-treated bednets and curtains for pre-venting malaria. Cochrane review. Cochrane Database Sys-tematic Reviews CD000363, 2004.

11. Global burden of disease: 2004 update. Geneva, World Health Organization, 2008 (www.who.int/healthinfo/bodestimates /en/index.html).

12. Mendis K et al. The neglected burden of Pasmodium vivax malaria. American Journal of Tropical Medicine and Hygiene,2001, 64: 97-106.

13. Rowe AK et al. The burden of malaria mortality among Afri-can children in the year 2000. International Journal of Epide-miology, 2006, 35:691–704.

14. Ross A et al. An epidemiologic model of severe morbidity and mortality caused by Plasmodium falciparum. American Journal of Tropical Medicine and Hygiene, 2006, 75(Suppl 2):.63–73.

ANNEX 2

Estimated and reported cases and deaths, 2006


Ann

ex 2

: Est

imat

ed a

nd re

port

ed c

ases

and

dea

ths,

200

6

WH

O re

gion

Cou

ntry

/are

aPo

pula

tion

Feve

r cas

esM

alar

ia c

ases

Mal

aria

deat

hsM

etho

dus

edO

utpa

tient

mal

aria

cas

esIn

patie

ntm

alar

ia c

ases

Mal

aria

attr

ibut

edde

aths

Cas

es a

tco

mm

unity

leve

l

Mic

. slid

es/

RD

Tsta

ken

Mic

. slid

es/

RD

Ts p

ositi

veM

ixed

infe

ctio

ns

Afr

ica

Alg

eria

33 3

51 1

36 -

00

ME

RG

--

--

--

--

-

Ang

ola

16 5

57 0

5010

062

166

3 55

4 90

821

130

ME

RG

2 28

3 09

7-

10 2

20-

106

801

1 08

2 39

8-

--

Ben

in8

759

653

9 43

7 98

63

238

973

12 7

70M

ER

G86

1 84

7-

1 39

2-

--

--

-

Bot

swan

a1

858

162

63 5

196

722

30H

MIS

19 0

00-

42-

19 0

002

500

--

-

Bur

kina

Fas

o14

358

500

23 0

15 0

036

226

667

25 6

25M

ER

G2

060

867

306

392

8 08

3-

122

047

44 2

65-

--

Bur

undi

8 17

3 07

07

132

386

2 27

0 87

27

662

HM

IS2

161

483

328

587

434

-29

1 18

815

7 73

8-

--

Cam

eroo

n18

174

696

14 7

04 5

905

091

300

21 1

46M

ER

G63

4 50

775

904

930

--

--

--

Cap

e V

erde

518

566

- -

1M

ER

G7

547

08

729

6161

00

Cen

tral A

frica

n R

epub

lic4

264

804

5 42

9 43

51

574

295

4 26

8M

ER

G11

4 40

329

667

865

--

--

--

Cha

d10

468

177

7 09

5 09

14

178

935

18 0

59M

ER

G46

233

52 3

4881

5-

63 8

1513

4 06

5-

--

Com

oros

818

434

603

405

205

208

293

ME

RG

54 8

3019

592

56-

--

--

-

Con

go3

689

299

3 89

1 23

61

331

668

4 56

6M

ER

G15

7 75

7-

253

--

--

--

Côt

e d'

Ivoi

re18

914

476

21 5

71 9

697

028

990

19 5

57M

ER

G1

253

408

2 52

7-

--

--

--

Dem

ocra

tic R

epub

lic o

f the

Con

go60

643

888

75 9

87 3

9623

619

960

96 1

13M

ER

G5

008

956

139

789

24 3

470

0-

--

-

Equ

ator

ial G

uine

a49

5 64

057

1 46

519

3 34

11

091

ME

RG

--

--

--

--

-

Erit

rea

4 69

2 11

516

6 16

318

964

75H

MIS

10 1

483

931

1771

775

46 0

966

541

5 75

079

1-

Eth

iopi

a81

020

610

44 9

64 7

9512

405

124

40 9

63H

MIS

3 75

9 96

038

427

1 35

7-

1 10

3 12

050

9 80

429

7 76

621

2 03

823

122

Gab

on1

310

818

1 13

9 92

438

6 50

61

255

ME

RG

118

104

-79

--

--

--

Gam

bia

1 66

3 03

21

192

196

469

382

1 75

9M

ER

G26

6 18

8-

--

--

--

-

Gha

na23

008

442

20 3

41 7

167

282

377

25 0

75M

ER

G3

511

452

122

928

2 83

2-

-47

2 25

5-

--

Gui

nea

9 18

1 33

811

226

052

3 76

6 47

815

081

ME

RG

834

835

--

-0

12 4

16-

--

Gui

nea-

Bis

sau

1 64

5 52

81

917

545

603

211

2 95

9M

ER

G14

8 72

0-

507

-45

804

17 5

21-

--

Ken

ya36

553

490

30 1

92 9

2411

341

750

27 0

49H

MIS

7 95

8 70

4-

--

--

--

-

Libe

ria3

578

922

2 54

1 35

11

459

884

6 12

8M

ER

G1

105

272

-87

7-

1 04

5 40

576

1 09

576

1 09

5-

-

Mad

agas

car

19 1

59 0

104

371

633

642

957

2 28

2H

MIS

1 01

2 63

98

820

590

-35

278

2 82

2-

--

Mal

awi

13 5

70 7

1413

343

182

4 52

7 65

112

950

ME

RG

4 20

4 46

814

1 71

07

132

--

--

--

Mal

i11

968

377

13 2

50 1

504

317

487

24 0

73M

ER

G1

022

592

-1

914

--

--

--

Mau

ritan

ia3

043

638

19 9

46 3

4955

9 48

42

588

ME

RG

188

025

10 7

3667

-31

013

1 06

1-

--

Mau

ritiu

s1

251

528

-0

-H

MIS

--

--

--

--

-

Moz

ambi

que

20 9

71 4

4622

234

003

7 43

2 53

919

211

ME

RG

6 33

5 75

7-

5 04

2-

140

371

46 3

22-

--

Nam

ibia

2 04

6 55

312

5 10

834

761

952

ME

RG

265

595

-57

1-

--

--

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ken

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ctio

ns

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le a

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tory

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6

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r cas

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aria

cas

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patie

ntm

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ia c

ases

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aria

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ibut

edde

aths

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es a

tco

mm

unity

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l

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es/

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ken1

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. slid

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veM

ixed

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ctio

ns

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orte

d pr

obab

le a

nd c

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med

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orte

d la

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tory

-con

firm

edEs

timat

es

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st A

sia

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155

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09 8

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592

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4 49

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9

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e 1 :

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DTs

take

n m

ay b

e lo

wer

than

Mic

. slid

es/R

DTs

pos

itive

if a

cou

ntry

did

not

repo

rt nu

mbe

r of R

DTs

exa

min

ed (t

aken

) –

N

ote:

May

otte

is o

ne o

f the

109

cou

ntrie

s/ar

eas

with

mal

ario

us a

reas

but

is n

ot in

clud

ed in

the

anne

xes.

ANNEX 3

a. Reported malaria cases, 1990–2007

b. Reported malaria deaths, 1990–2007


Ann

ex 3

A: R

epor

ted

mal

aria

cas

es, 1

990-

2007

WH

O re

gion

Cou

ntry

/are

a19

9019

9119

9219

9319

9419

9519

9619

9719

9819

9920

0020

0120

0220

0320

0420

0520

0620

07

Afr

ica

Alg

eria

152

229

106

8420

610

722

119

7-

701

541

435

307

--

--

-

Ang

ola

243

673

1 14

3 70

178

2 98

872

2 98

166

7 37

615

6 60

3-

893

232

1 16

9 02

81

471

993

1 63

5 88

41

249

767

1 86

2 66

23

246

258

2 48

9 17

02

329

316

2 28

3 09

7-

Ben

in92

870

118

796

290

868

403

327

546

827

579

300

623

396

670

857

650

025

709

348

707

408

717

290

782

818

819

256

853

034

803

462

861

847

-

Bot

swan

a10

750

14 3

644

995

55 3

3129

591

17 5

9980

004

101

887

59 6

9672

640

71 4

0348

237

28 8

5823

6 77

422

404

11 2

4219

000

-

Bur

kina

Fas

o49

6 51

344

8 91

742

0 18

650

2 27

547

2 35

550

1 02

058

2 65

867

2 75

272

1 48

086

7 86

61

032

886

352

587

1 18

8 87

01

443

184

1 54

6 64

41

615

695

2 06

0 86

72

487

633

Bur

undi

92 8

7056

8 93

877

3 53

982

8 42

983

1 48

193

2 79

497

4 22

667

0 85

768

7 30

11

936

584

3 05

7 23

93

365

640

2 64

9 03

92

259

694

1 74

9 89

22

361

734

2 16

1 48

3-

Cam

eroo

n86

9 04

878

7 79

666

4 41

347

8 69

318

9 06

678

4 32

193

1 31

178

7 79

666

4 41

3-

--

--

-27

7 41

363

4 50

7-

Cap

e V

erde

6980

3844

2112

777

2041

29-

152

64

77

-

Cen

tral A

frica

n R

epub

lic17

4 43

612

5 03

889

930

82 0

7282

057

100

962

95 2

5999

718

105

664

127

964

89 6

1412

3 31

2-

78 0

9412

9 36

713

1 85

611

4 40

3-

Cha

d21

2 55

424

6 41

022

9 44

423

4 86

927

8 22

529

3 56

427

8 04

834

3 18

639

5 20

539

2 81

536

9 26

338

6 19

743

933

45 1

9531

929

41 2

3746

233

-

Com

oros

--

-12

012

13 8

6015

707

15 5

09-

3 84

49

793

-3

718

--

43 9

1829

554

54 8

30-

Con

go32

428

32 3

9121

121

15 5

0435

957

28 0

0814

000

9 49

117

122

--

--

--

-15

7 75

7-

Côt

e d'

Ivoi

re51

1 91

646

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A: R

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2007

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Ann

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A: R

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ted

mal

aria

cas

es, 1

990-

2007

WH

O re

gion

Cou

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a19

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9219

9319

9419

9519

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9719

9819

9920

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y co

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Not

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eath

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porte

d be

fore

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1 ca

n be

pro

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nfirm

ed o

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nfirm

ed d

eath

s de

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ing

on th

e co

untry

.

ANNEX 4

a. Recommended policies and strategies for malaria control, 2007

b. Antimalarial drug policy, 2008


Ann

ex 4

A: R

ecom

men

ded

polic

ies

and

stra

tegi

es fo

r mal

aria

con

trol

, 200

7M

alar

ia in

preg

nanc

y

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Dis

trib

utio

n -

Free

Targ

etin

gpo

pula

tion

– A

ll

Targ

etin

gpo

pula

tion

–U

nder

5 y

ears

old

and

preg

nant

wom

en

Inse

ctic

ide-

resi

stan

cem

gmt.

Impl

emen

tatio

nD

DT

is u

sed

for

IRS

AC

T po

licy

adop

ted

AC

T is

free

inpu

blic

sec

tors

Para

sito

logi

cal

conf

irmat

ion

for

all a

ge g

roup

s

Ora

l art

emis

inin

mon

othe

rapi

esba

nned

IPT

stra

tegy

used

to p

reve

ntm

alar

ia d

urin

gpr

egna

ncy

Afr

ica

Alg

eria

--

--

--

--

--

Ang

ola

YY

NY

NY

YY

YY

Ben

inY

YN

YN

YN

YN

YB

otsw

ana

NN

YY

NY

YY

YN

Bur

kina

Fas

oY

YY

YN

YY

YN

YB

urun

diY

Y-

YN

YN

YN

NC

amer

oon

YY

NY

NY

NY

NY

Cap

e V

erde

YY

YN

NN

YN

N-

Cen

tral A

frica

n R

epub

licY

YY

NN

YY

YY

YC

had

YY

YN

NY

NY

YY

Com

oros

YY

NY

NY

YY

NY

Con

goY

Y-

NN

YN

YN

YC

ôte

d'Iv

oire

YY

NY

NY

NY

NY

Dem

ocra

tic R

epub

lic o

f the

Con

goY

YY

--

YY

NN

YE

quat

oria

l Gui

nea

--

--

-Y

--

--

Erit

rea

YY

YY

YY

YY

YN

Eth

iopi

aY

YY

YY

YY

YY

YG

abon

YY

-N

NY

YY

YY

Gam

bia

YY

YN

NY

NY

YY

Gha

naY

N-

YN

YN

YN

YG

uine

aY

NN

YN

YN

YN

YG

uine

a-B

issa

u-

Y-

--

YN

NN

YK

enya

YY

NN

NY

NN

YY

Libe

ria-

--

--

Y-

--

-M

adag

asca

rY

YY

YN

YY

YY

YM

alaw

iY

YN

NN

YN

YY

YM

ali

YY

N-

-Y

YN

YY

Mau

ritan

iaY

YY

NN

YN

YN

YM

aurit

ius

--

--

--

--

--

Moz

ambi

que

YY

NN

YY

NY

YY

Nam

ibia

YY

NY

YY

NN

YY

Nig

erY

NN

YN

YY

NY

YN

iger

iaY

Y-

NN

YY

YY

YR

wan

daY

YN

YN

YY

YY

-S

ao T

ome

and

Prin

cipe

YY

YY

NY

NY

NY

Sen

egal

YY

YY

NY

YY

NY

Sie

rra

Leon

eY

YY

NN

YY

YY

YS

outh

Afri

ca-

--

YY

YN

YY

NS

waz

iland

YY

NY

YN

YN

N-

Togo

YN

NY

NY

NY

NY

Uga

nda

YY

NY

NY

YY

YY

Uni

ted

Rep

ublic

of T

anza

nia

Y-

NN

NY

NN

YY

Zam

bia

YY

YY

YY

YY

YY

Zim

babw

eY

YY

-Y

YN

NY

Y

Inse

ctic

ide-

trea

ted

nets

Indo

or re

sidu

al s

pray

ing

Trea

tmen

t


Ann

ex 4

A: R

ecom

men

ded

polic

ies

and

stra

tegi

es fo

r mal

aria

con

trol

, 200

7M

alar

ia in

preg

nanc

y

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Dis

trib

utio

n -

Free

Targ

etin

gpo

pula

tion

– A

ll

Targ

etin

gpo

pula

tion

–U

nder

5 y

ears

old

and

preg

nant

wom

en

Inse

ctic

ide-

resi

stan

cem

gmt.

Impl

emen

tatio

nD

DT

is u

sed

for

IRS

AC

T po

licy

adop

ted

AC

T is

free

inpu

blic

sec

tors

Para

sito

logi

cal

conf

irmat

ion

for

all a

ge g

roup

s

Ora

l art

emis

inin

mon

othe

rapi

esba

nned

IPT

stra

tegy

used

to p

reve

ntm

alar

ia d

urin

gpr

egna

ncy

Inse

ctic

ide-

trea

ted

nets

Indo

or re

sidu

al s

pray

ing

Trea

tmen

t

Am

eric

asA

rgen

tina

NN

NN

N-

YN

N-

Bel

ize

--

--

N-

Y-

--

Bol

ivia

YN

YY

NY

YY

Y-

Bra

zil

NY

YY

NY

YY

Y-

Col

ombi

a-

-Y

YN

YY

--

-C

osta

Ric

aN

NN

-N

-Y

--

-D

omin

ican

Rep

ublic

NN

NY

NN

YN

N-

Ecu

ador

--

YY

NY

Y-

Y-

El S

alva

dor

-Y

YY

N-

YY

--

Fren

ch G

uian

aN

--

N-

--

-Y

-G

uate

mal

a-

--

-N

-Y

--

-G

uyan

aY

YY

NY

YY

-Y

-H

aiti

YN

YY

--

YY

N-

Hon

dura

sY

--

-N

-Y

--

-Ja

mai

ca-

--

--

--

--

Mex

ico

--

YY

N-

Y-

--

Nic

arag

uaY

YY

NN

-Y

YN

-P

anam

aN

NN

YN

-Y

--

-P

arag

uay

NN

NN

NN

Y-

--

Per

u-

-Y

-N

YY

--

-S

urin

ame

YY

YN

NY

YY

Y-

Ven

ezue

la (B

oliv

aria

n R

epub

lic o

f)-

-Y

-N

-Y

--

-Ea

ster

n M

edite

rran

ean

Afg

hani

stan

--

--

-Y

--

--

Djib

outi

--

--

-Y

--

--

Egy

pt-

--

--

Y-

--

-Ira

n (Is

lam

ic R

epub

lic o

f)N

YY

YN

YY

YY

-Ira

qN

YY

YN

YY

YY

-M

oroc

co-

--

--

Y-

--

-O

man

--

--

--

--

--

Pak

ista

nN

YY

YN

YN

YY

-S

audi

Ara

bia

YY

YY

NY

YY

Y-

Som

alia

YY

YN

N-

NY

YY

Sud

anY

YY

YN

YY

YY

YS

yria

n A

rab

Rep

ublic

--

--

--

--

--

Yem

enY

YN

YN

YY

YY

-Eu

rope

Arm

enia

YY

NN

N-

YN

N-

Aze

rbai

jan

NN

NN

N-

YN

N-

Geo

rgia

NN

NN

N-

YN

N-

Kyr

gyzs

tan

YY

NY

NY

NN

-R

ussi

an F

eder

atio

n-

--

--

--

--

Tajik

ista

nY

YY

YN

YY

YY

-Tu

rkey

NN

NY

N-

Y-

--

Turk

men

ista

nN

NY

YN

-Y

NN

-U

zbek

ista

nN

YY

YN

YN

N-


Ann

ex 4

A: R

ecom

men

ded

polic

ies

and

stra

tegi

es fo

r mal

aria

con

trol

, 200

7M

alar

ia in

preg

nanc

y

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Dis

trib

utio

n -

Free

Targ

etin

gpo

pula

tion

– A

ll

Targ

etin

gpo

pula

tion

–U

nder

5 y

ears

old

and

preg

nant

wom

en

Inse

ctic

ide-

resi

stan

cem

gmt.

Impl

emen

tatio

nD

DT

is u

sed

for

IRS

AC

T po

licy

adop

ted

AC

T is

free

inpu

blic

sec

tors

Para

sito

logi

cal

conf

irmat

ion

for

all a

ge g

roup

s

Ora

l art

emis

inin

mon

othe

rapi

esba

nned

IPT

stra

tegy

used

to p

reve

ntm

alar

ia d

urin

gpr

egna

ncy

Inse

ctic

ide-

trea

ted

nets

Indo

or re

sidu

al s

pray

ing

Trea

tmen

t

Sout

h-Ea

st A

sia

Ban

glad

esh

YY

YN

NY

YY

Y-

Bhu

tan

NY

YN

NY

YY

Y-

Dem

ocra

tic P

eopl

e's

Rep

ublic

of K

orea

YN

YY

Y-

NN

N-

Dem

ocra

tic R

epub

lic o

f Tim

or-L

este

YY

YN

NY

YY

Y-

Indi

aY

YN

YY

YY

YY

-In

done

sia

YY

YY

NY

YY

Y-

Mya

nmar

NY

YN

YY

YY

Y-

Nep

al-

YY

YN

YY

YY

-S

ri La

nka

YY

YY

NY

YY

--

Thai

land

NY

YY

NY

YY

Y-

Wes

tern

Pac

ific

Cam

bodi

aY

YY

NN

YY

YY

-C

hina

NN

YN

NY

YN

N-

Lao

Peo

ple'

s D

emoc

ratic

Rep

ublic

NN

YN

NY

YY

Y-

Mal

aysi

aN

YY

YN

YY

NN

-P

apua

New

Gui

nea

NY

YN

YY

NY

Y-

Phi

lippi

nes

YY

YY

NY

YN

Y-

Rep

ublic

of K

orea

--

--

--

--

--

Sol

omon

Isla

nds

YY

YY

NY

YN

Y-

Van

uatu

YY

YN

NY

NY

--

Vie

t Nam

-Y

-Y

NY

NN

Y-

(Y) =

Act

ually

impl

emen

ted.

(N) =

Not

impl

emen

ted.

(-) =

Que

stio

n no

t ans

wer

ed o

r not

app

licab

le.


Ann

ex 4

B: A

ntim

alar

ial d

rug

polic

y, 2

008

P.vi

vax

WH

O re

gion

Cou

ntry

/are

aU

ncom

plic

ated

Seve

rePr

even

tion

durin

g pr

egna

ncy

Trea

tmen

tA

fric

aA

lger

ia-

-C

QA

ngol

aA

LQ

N(7

d)S

P(IP

T)-

Ben

inA

LQ

N(7

d)S

P(IP

T)-

Bot

swan

aA

L*Q

N(7

d)C

Q+P

G-

Bur

kina

Fas

oA

L*Q

N(7

d)S

P(IP

T)-

Bur

undi

AS

+AQ

QN

(7d)

--

Cam

eroo

nA

S+A

QQ

N(7

d)S

P (I

PT)

-C

ape

Ver

deC

QQ

N(7

d)C

Q w

eekl

y-

Cen

tral A

frica

n R

epub

licA

L*Q

N(7

d)S

P(IP

T)-

Cha

dA

S+A

Q*

/AL*

QN

(7d)

SP

(IPT)

-C

omor

osA

LQ

N(7

d)S

P(IP

T)-

Con

goA

S+A

Q*

QN

(7d)

SP

(IPT)

-C

ôte

d'Iv

oire

AS

+AQ

*Q

N(7

d)S

P(IP

T)-

Dem

ocra

tic R

epub

lic o

f the

Con

goA

S+A

QQ

N(7

d)S

P (I

PT)

-E

quat

oria

l Gui

nea

AS

+AQ

QN

(7d)

--

Erit

rea

CQ

+SP

QN

(7d)

-C

Q+P

QE

thio

pia

AL

QN

(7d)

-C

QG

abon

AS

+AQ

QN

(7d)

SP

(IPT)

-G

ambi

aA

LQ

N(7

d)S

P(IP

T)-

Gha

naA

S+A

QQ

N(7

d)S

P (I

PT)

-G

uine

aA

S+A

Q*

QN

(7d)

SP

(IPT)

-G

uine

a-B

issa

uA

L*Q

N(7

d)S

P(IP

T)-

Ken

yaA

LQ

N(7

d)S

P (I

PT)

-Li

beria

AS

+AQ

QN

(7d)

SP

(IPT)

-M

adag

asca

rA

S+A

QQ

N(7

d)S

P (I

PT)

-M

alaw

iA

LQ

N(7

d)S

P (I

PT)

-M

ali

AL*

QN

(7d)

SP

(IPT)

-M

aurit

ania

AS

+AQ

*Q

N(7

d)-

-M

aurit

ius

--

-C

QM

ozam

biqu

eA

L*Q

N(7

d)S

P (I

PT)

-N

amib

iaA

LQ

N(7

d)S

P(IP

T)-

Nig

erA

LQ

N(7

d)S

P(IP

T)-

Nig

eria

AL

QN

(7d)

SP

(IPT)

-R

wan

daA

LQ

N(7

d)S

P (I

PT)

-S

ao T

ome

and

Prin

cipe

AS

+AQ

QN

(7d)

SP

(IP

T)-

Sen

egal

AS

+AQ

QN

(7d)

SP

(IP

T)-

Sie

rra

Leon

eA

S+A

QQ

N(7

d)S

P(IP

T)-

Sou

th A

frica

AL

QN

(7d)

CQ

+PG

-S

waz

iland

CQ

QN

(7d)

CQ

+PG

-To

goA

LQ

N(7

d)S

P(IP

T)-

Uga

nda

AL

QN

(7d)

SP

(IP

T)-

Uni

ted

Rep

ublic

of T

anza

nia

AL

/ AS

+AQ

QN

(7d)

SP

(IP

T)-

Zam

bia

AL

QN

(7d)

SP

(IP

T)-

Zim

babw

eA

L*Q

N(7

d)S

P (I

PT)

-A

mer

icas

Arg

entin

a-

-C

Q+P

QB

eliz

e-

--

CQ

+PQ

Bol

ivia

--

-C

Q+P

QB

razi

l-

AS

or A

M o

r QN

-C

Q+P

Q(7

d)C

olom

bia

-Q

N(7

d)-

CQ

+PQ

Cos

ta R

ica

--

-C

Q+P

QD

omin

ican

Rep

ublic

--

--

Ecu

ador

--

-C

Q+P

QE

l Sal

vado

r-

--

CQ

+PQ

P. fa

lcip

arum

* P

olic

y ad

opte

d, m

ay n

ot y

et b

e im

plem

ente

d


Ann

ex 4

B: A

ntim

alar

ial d

rug

polic

y, 2

008

P.vi

vax

WH

O re

gion

Cou

ntry

/are

aU

ncom

plic

ated

Seve

rePr

even

tion

durin

g pr

egna

ncy

Trea

tmen

t

P. fa

lcip

arum

Fren

ch G

uian

a-

--

CQ

+PQ

Gua

tem

ala

--

-C

Q+P

QG

uyan

a-

--

CQ

+PQ

Hai

ti-

--

-H

ondu

ras

--

-C

Q+P

QM

exic

o-

--

CQ

+PQ

Nic

arag

ua-

QN

+CL

-C

Q+P

Q(7

d)P

anam

a-

--

CQ

+PQ

Par

agua

y-

--

CQ

+PQ

Per

u-

--

CQ

+PQ

Sur

inam

e-

--

CQ

+PQ

Ven

ezue

la (B

oliv

aria

n R

epub

lic o

f)-

--

CQ

+PQ

East

ern

Med

iterr

anea

nA

fgha

nist

anC

Q+S

PQ

N(7

d)/A

S+S

P-

CQ

Djib

outi

AS

+SP

*Q

N-

CQ

+PQ

(14d

)E

gypt

-Q

N(7

d)-

CQ

+PQ

(14d

)Ira

n (Is

lam

ic R

epub

lic o

f)-

QN

or A

S-

CQ

+PQ

(14d

)Ira

q-

QN

(7d)

-C

Q+P

Q(1

4d)

Mor

occo

-Q

N(7

d)-

CQ

+PQ

(14d

)O

man

-Q

N(7

d)-

CQ

+PQ

(14d

)P

akis

tan

AS

+SP

QN

/AM

-C

Q+P

Q(5

d)S

audi

Ara

bia

-Q

N(7

d)-

CQ

+PQ

(14d

)S

omal

iaA

S+S

PQ

NS

P(IP

T)-

Sud

anA

S+S

P, A

S+A

QQ

N/A

M/A

S+S

PS

P(IP

T)C

Q+P

Q(1

4d)

Syr

ian

Ara

b R

epub

lic-

QN

(7d)

-C

Q+P

Q(1

4d)

Yem

enA

S+S

PQ

N(7

d)-

CQ

+PQ

(14d

)Eu

rope

Arm

enia

--

CQ

+PQ

(14d

)A

zerb

aija

n-

--

CQ

+PQ

(14d

)G

eorg

ia-

--

CQ

+PQ

(14d

)Ta

jikis

tan

-Q

N(7

d)-

CQ

+PQ

(14d

)Tu

rkey

--

-C

Q+P

Q(1

4d)

Turk

men

ista

n-

--

CQ

+PQ

(14d

)

* P

olic

y ad

opte

d, m

ay n

ot y

et b

e im

plem

ente

d


Ann

ex 4

B: A

ntim

alar

ial d

rug

polic

y, 2

008

WH

O re

gion

Cou

ntry

/are

aU

ncom

plic

ated

Seve

rePr

even

tion

durin

g pr

egna

ncy

Trea

tmen

tSo

uth-

East

Asi

aB

angl

ades

hC

Q+P

QQ

N/A

M-

CQ

+PQ

(14d

)B

huta

n-

QN

/AM

-C

Q+P

Q(1

4d)

Dem

ocra

tic P

eopl

e's

Rep

ublic

of K

orea

--

-C

Q+P

Q(1

4d)

Dem

ocra

tic R

epub

lic o

f Tim

or-L

este

CQ

+PQ

QN

/AM

-C

Q+P

Q(1

4d)

Indi

aC

Q+P

QQ

N/A

M-

CQ

+PQ

(14d

)In

done

sia

CQ

+PQ

QN

/AM

-C

Q+P

Q(1

4d)

Mya

nmar

CQ

QN

/AS

-C

Q+P

Q(1

4d)

Nep

alC

Q+P

QQ

N-

CQ

+PQ

(14d

)S

ri La

nka

-Q

N(7

d)-

CQ

+PQ

(14d

)Th

aila

nd-

QN

/AS

-C

Q+P

Q(1

4d)

Wes

tern

Pac

ific

Cam

bodi

aA

S+M

QA

M+M

Q-

CQ

Chi

naC

Q+A

T(5d

)A

M; P

R; A

S-

CQ

+PQ

(8d)

, C

Q+P

Q(5

d)La

o P

eopl

e's

Dem

ocra

tic R

epub

licC

QA

S+A

LC

Q (w

eekl

y)/S

P(IP

T)C

Q+P

Q(1

4d)

Mal

aysi

a-

QN

+T-

CQ

+PQ

(14d

)P

apua

New

Gui

nea

AL*

AS

/AM

+AL*

CQ

wee

kly

CQ

+PQ

(14d

)P

hilip

pine

sA

L*Q

N+T

CQ

(wee

kly)

/SP

(IPT)

CQ

+PQ

(14d

)R

epub

lic o

f Kor

ea-

--

CQ

+PQ

(14d

)S

olom

on Is

land

s-

AS

+AL

CQ

AL+

PQ

(14d

)V

anua

tuA

L*Q

N(7

d)C

Q (w

eekl

y)A

L+P

Q(1

4d)

Vie

t Nam

AS

/CQ

AS

inj/

QN

inj

CQ

(wee

kly)

CQ

+PQ

(14d

)

AL=

Arte

met

her-

Lum

efan

trine

DA

=Dih

ydro

arte

mis

inin

PR

=Pyr

onar

idin

eA

M=A

rtem

ethe

rIP

T=In

term

itten

t Pre

vent

ive

Trea

tmen

tQ

N=Q

uini

neA

Q=A

mod

iaqu

ine

MQ

=Mef

loqu

ine

SP

=Sul

fado

xine

-pyr

imet

ham

ine

AS

=Arte

suna

teP

G=P

rogu

anil

T=Te

tracy

clin

eC

= C

linda

myc

inP

IP=

Pip

eraq

uine

TRI=

Trim

etho

prim

CQ

=Chl

oroq

uine

PM

=Pyr

imet

ham

ine

(use

d on

ly in

Chi

na)

D=

Dox

ycyc

line

PQ

= P

rimaq

uine

(nor

mal

ly u

sed

at 1

5 m

g/da

y, b

ut C

hina

pol

icy

is 2

2.5

mg/

day)

*Pol

icy

adop

ted.

ANNEX 5

Operational coverage of insecticide-treated nets, indoor residual spraying and antimalarial treatment, 2004–2006


Ann

ex 5

: Ope

ratio

nal c

over

age

of in

sect

icid

e tr

eate

d ne

ts, i

ndoo

r res

idua

l spr

ayin

g an

d an

timal

aria

l tre

atm

ent,

2004

-200

7

WH

O re

gion

Cou

ntry

/are

aYe

ar

No.

of I

TN +

LLIN

sol

d or

deliv

ered

No.

of L

LIN

sol

dor

del

iver

edN

o. o

f ITN

sol

dor

del

iver

ed%

ITN

cove

rage

No.

of

hous

ehol

dssp

raye

d

No.

of

peop

lepr

otec

ted

by IR

S%

IRS

cove

rage

Any

1st

-line

trea

tmen

tco

urse

sde

liver

ed(in

clud

ing

AC

T)

AC

Ttr

eatm

ent

cour

ses

deliv

ered

% A

nyan

timal

aria

lco

vera

geto

tal

% A

CT

cove

rage

tota

l

% A

nyan

timal

aria

lco

vera

gepu

blic

% A

CT

cove

rage

publ

icA

fric

aA

lger

ia20

04-

--

--

--

--

--

--

2005

--

--

--

--

--

--

-20

06-

--

--

--

--

--

--

2007

--

--

--

--

--

--

-A

ngol

a20

04-

--

--

--

--

--

--

2005

--

--

118

000

590

000

3.7

--

--

--

2006

984

760

826

656

158

104

11.9

132

436

780

257

4.0

1 70

0 00

01

736

200

16.9

17.3

27.8

28.4

2007

--

--

--

--

--

--

-B

enin

2004

--

659

254

--

--

--

--

--

2005

--

--

--

--

--

--

-20

06-

-76

500

--

--

-49

525

-0.

5-

1.3

2007

--

--

--

--

--

--

-B

otsw

ana

2004

45 1

90-

45 1

90-

136

670

6578

.923

00.

1-

0.8

-20

05-

--

-12

0 62

759

68.9

-0

--

--

2006

4 00

01

500

2 50

00.

912

7 74

672

72.1

89 0

230

297.

30

3083

.80

2007

--

--

--

--

--

--

-B

urki

na F

aso

2004

-0

125

000

--

--

5 19

1 73

80

--

--

2005

-40

1 50

050

1 50

013

.0-

--

4 16

7 90

80

--

--

2006

-12

1 10

029

1 10

011

.3-

--

3 93

0 29

60

--

--

2007

-13

000

11 0

007.

4-

--

4 98

1 27

081

1 50

721

.13.

465

.810

.7B

urun

di20

0442

0 79

363

993

356

800

14.6

--

-70

000

0-

--

-20

0547

6 11

947

6 11

967

6 31

840

.079

784

478

704

6.5

1 22

9 17

01

221

290

--

--

2006

274

800

274

800

62 6

0827

.5-

--

1 70

7 65

11

707

651

24.1

24.1

51.4

51.4

2007

--

--

--

--

--

--

-C

amer

oon

2004

-0

140

443

--

--

--

--

--

2005

-0

404

755

--

--

3 58

3 33

20

24.9

-70

.7-

2006

1 09

7 51

016

800

1 08

0 71

012

.1-

--

-0

-0

-0

2007

--

--

--

--

--

--

-C

ape

Ver

de20

040

00

--

--

-0

--

--

2005

00

0-

--

--

0-

--

-20

060

00

--

--

-0

-0

-0

2007

--

--

--

--

--

--

-C

entra

l Afri

can

Rep

ublic

2004

--

6 43

4-

--

-95

4 85

786

886

18.2

1.7

48.8

4.4

2005

--

0-

--

--

--

--

-20

06-

125

828

134

5.9

--

--

--

--

-20

07-

--

--

--

--

--

--

Cha

d20

0410

000

10 0

00-

0.4

--

--

--

--

-20

0512

8 29

310

000

118

293

3.0

--

-40

586

40 5

86-

--

-20

0626

7 00

019

9 00

011

0 00

06.

439

500

-2.

110

4 71

710

4 71

71.

51.

512

.512

.520

07-

--

--

--

--

--

--

Com

oros

2004

11 0

0011

000

11 0

005.

7-

--

--

--

--

2005

25 0

000

25 0

009.

0-

--

960

163

17 2

18-

--

-20

0678

750

078

750

21.9

--

-85

5 00

054

720

141.

79.

536

2.2

24.4

2007

--

--

--

--

--

--

-C

ongo

2004

--

2 45

0-

--

--

--

--

-20

05-

--

--

--

--

--

--

2006

-20

0 00

0-

10.8

--

--

--

--

-20

07-

--

--

--

--

--

--

Côt

e d'

Ivoi

re20

0412

000

12 0

00-

0.2

--

-0

--

--

-


Ann

ex 5

: Ope

ratio

nal c

over

age

of in

sect

icid

e tr

eate

d ne

ts, i

ndoo

r res

idua

l spr

ayin

g an

d an

timal

aria

l tre

atm

ent,

2004

-200

7

WH

O re

gion

Cou

ntry

/are

aYe

ar

No.

of I

TN +

LLIN

sol

d or

deliv

ered

No.

of L

LIN

sol

dor

del

iver

edN

o. o

f ITN

sol

dor

del

iver

ed%

ITN

cove

rage

No.

of

hous

ehol

dssp

raye

d

No.

of

peop

lepr

otec

ted

by IR

S%

IRS

cove

rage

Any

1st

-line

trea

tmen

tco

urse

sde

liver

ed(in

clud

ing

AC

T)

AC

Ttr

eatm

ent

cour

ses

deliv

ered

% A

nyan

timal

aria

lco

vera

geto

tal

% A

CT

cove

rage

tota

l

% A

nyan

timal

aria

lco

vera

gepu

blic

% A

CT

cove

rage

publ

icC

ôte

d'Iv

oire

2005

53 6

96-

53 6

960.

8-

--

971

683

--

--

-20

0637

1 81

633

6 00

037

1 81

67.

6-

--

1 10

2 87

94

875

--

--

2007

--

--

--

-72

1 31

447

6 20

33.

32.

212

.48.

2D

emoc

ratic

Rep

ublic

of t

he C

ongo

2004

877

161

--

3.1

--

-0

--

--

-20

0579

1 13

5-

-2.

7-

--

0-

--

--

2006

2 37

9 38

4-

-7.

8-

--

1 68

1 21

11

681

211

2.2

2.2

6.9

6.9

2007

--

--

--

--

--

--

-E

quat

oria

l Gui

nea

2004

--

--

--

--

--

--

-20

05-

--

--

--

--

--

--

2006

--

--

--

--

--

--

-20

07-

--

--

--

--

--

--

Erit

rea

2004

215

000

-21

5 00

0-

92 1

0725

9 42

010

.630

2 47

03

117

--

--

2005

107

657

107

657

-4.

889

134

260

263

9.8

290

500

4 40

0-

--

-20

0680

673

80 6

73-

8.0

68 7

7820

8 37

77.

325

0 81

025

000

292.

733

.221

40.3

242.

720

07-

--

--

--

--

--

--

Eth

iopi

a20

040

00

-84

5 69

34

228

465

8.1

9 72

5 00

025

000

--

--

2005

4 24

3 15

74

243

157

015

.878

2 58

13

912

903

7.3

3 50

0 00

03

193

993

--

--

2006

9 07

0 71

89

070

718

048

.31

196

897

5 98

4 48

510

.96

950

000

6 80

6 74

4-

--

-20

077

178

443

7 17

8 44

30

72.5

1 59

0 96

45

303

213

14.1

5 45

0 40

04

032

640

12.1

15.4

91.9

116.

4G

abon

2004

--

--

--

--

--

--

-20

05-

--

--

--

--

--

--

2006

326

548

--

49.8

--

--

--

--

-20

07-

--

--

--

--

--

--

Gam

bia

2004

171

149

-17

1 14

9-

--

--

--

--

-20

0532

7 62

310

3 91

122

3 71

240

.5-

--

--

--

--

2006

229

841

32 4

6619

7 37

540

.1-

--

--

--

--

2007

--

--

--

--

--

--

-G

hana

2004

375

000

150

000

225

000

3.4

--

--

--

--

-20

0561

8 85

561

8 85

5-

6.8

--

--

--

--

-20

062

100

000

2 10

0 00

0-

24.9

134

000

200

000

2.9

3 60

0 00

03

600

000

--

--

2007

1 47

7 53

81

477

538

-35

.715

4 00

024

0 00

03.

32

018

967

1 85

2 96

79.

78.

923

.621

.7G

uine

a20

0459

000

059

000

--

--

-0

-0

-0

2005

149

500

149

500

03.

39

872

-0.

7-

--

--

-20

0614

4 68

987

500

57 1

896.

42

800

-0.

2-

--

--

-20

07-

--

--

--

--

--

--

Gui

nea-

Bis

sau

2004

--

--

--

-14

9 46

70

--

--

2005

-20

000

-2.

5-

--

163

667

0-

--

-20

06-

182

906

-24

.7-

--

71 1

330

3.8

06.

40

2007

--

--

--

--

--

--

-K

enya

2004

1 16

9 60

08

100

1 16

1 50

08.

960

000

300

000

1.0

-0

--

--

2005

3 65

5 57

61

900

462

1 75

5 11

427

.193

000

465

000

1.5

723

333

723

333

--

--

2006

7 10

2 75

26

378

465

724

287

65.0

110

000

550

000

1.8

5 04

9 00

05

049

000

16.7

16.7

54.9

54.9

2007

--

--

691

841

3 45

9 20

710

.9-

--

--

-Li

beria

2004

-64

000

-3.

8-

--

--

--

--

2005

-15

0 00

0-

12.4

--

--

--

--

-20

06-

446

000

-36

.9-

--

-86

2 90

3-

35.6

-91

.120

07-

--

--

--

--

--

--

Mad

agas

car

2004

253

921

--

2.8

102

400

485

395

2.7

--

--

--

2005

871

570

--

9.4

250

000

409

155

6.3

--

--

--


Ann

ex 5

: Ope

ratio

nal c

over

age

of in

sect

icid

e tr

eate

d ne

ts, i

ndoo

r res

idua

l spr

ayin

g an

d an

timal

aria

l tre

atm

ent,

2004

-200

7

WH

O re

gion

Cou

ntry

/are

aYe

ar

No.

of I

TN +

LLIN

sol

d or

deliv

ered

No.

of L

LIN

sol

dor

del

iver

edN

o. o

f ITN

sol

dor

del

iver

ed%

ITN

cove

rage

No.

of

hous

ehol

dssp

raye

d

No.

of

peop

lepr

otec

ted

by IR

S%

IRS

cove

rage

Any

1st

-line

trea

tmen

tco

urse

sde

liver

ed(in

clud

ing

AC

T)

AC

Ttr

eatm

ent

cour

ses

deliv

ered

% A

nyan

timal

aria

lco

vera

geto

tal

% A

CT

cove

rage

tota

l

% A

nyan

timal

aria

lco

vera

gepu

blic

% A

CT

cove

rage

publ

icM

adag

asca

r20

061

616

200

2 03

8 00

0-

21.3

250

000

1 43

0 00

06.

1-

--

--

-20

072

942

563

--

50.6

248

269

1 43

0 00

05.

973

3 09

8-

16.3

-61

-M

alaw

i20

041

295

498

-1

295

498

--

--

-0

--

--

2005

815

620

-81

5 62

0-

--

-27

903

000

0-

--

-20

061

508

735

120

000

1 38

8 73

522

.2-

--

27 9

03 0

000

209.

10

800.

10

2007

--

--

--

--

--

--

-M

ali

2004

0-

-0.

0-

--

--

--

--

2005

572

556

572

556

-9.

9-

--

--

--

--

2006

90 9

0090

900

-11

.1-

--

--

--

--

2007

--

--

--

--

--

--

-M

aurit

ania

2004

--

13 1

78-

--

-0

0-

--

-20

05-

--

--

--

00

--

--

2006

-49

616

23 8

636.

9-

--

00

00

00

2007

--

--

--

--

--

--

-M

aurit

ius

2004

--

--

--

--

--

--

-20

05-

--

--

--

--

--

--

2006

--

--

--

--

--

--

-20

07-

--

--

--

--

--

--

Moz

ambi

que

2004

401

802

--

4.0

754

492

3 77

2 45

918

.4-

--

--

-20

0572

5 11

9-

-7.

11

250

375

6 51

1 18

429

.8-

--

--

-20

0668

3 41

0-

-6.

51

537

825

6 96

2 79

935

.9-

--

--

-20

07-

--

--

--

--

--

--

Nam

ibia

2004

68 0

0068

000

-16

.529

2 55

226

7 51

710

9.5

--

--

--

2005

120

000

120

000

-34

.055

0 85

431

9 44

120

3.5

--

--

--

2006

133

000

133

000

-46

.857

3 04

546

1 86

320

9.0

--

--

--

2007

--

--

--

--

--

--

-N

iger

2004

--

--

--

--

--

--

-20

05-

--

--

--

--

--

--

2006

-2

665

000

2 66

5 00

077

.6-

--

--

--

--

2007

--

--

--

-3

442

130

-32

.6-

113.

9-

Nig

eria

2004

3 27

9 00

03

279

000

1 04

5 23

06.

3-

--

3 31

0 22

9-

--

--

2005

5 08

6 93

41

807

934

3 27

9 00

011

.8-

--

3 53

2 10

8-

--

--

2006

8 85

3 58

93

766

655

5 08

6 93

419

.390

045

000

0.0

3 98

2 37

24

530

108

--

--

2007

3 22

5 59

41

003

573

2 22

2 02

111

.94

500

-0.

02

969

950

8 98

6 48

51.

95.

67.

622

.9R

wan

da20

0422

3 92

6-

223

926

--

--

--

--

--

2005

253

700

185

000

68 7

005.

5-

--

--

--

--

2006

1 95

7 72

01

957

720

-45

.3-

--

-68

4 99

0-

8.2

-41

.820

07-

--

--

--

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--

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Sao

Tom

e an

d P

rinci

pe20

04-

14 7

12-

19.6

--

--

--

--

-20

05-

11 7

18-

34.6

30 1

3113

9 81

698

.718

940

18 9

40-

--

-20

06-

52 8

47-

102.

226

501

132

186

85.4

14 3

6314

363

39.9

4212

813

4.7

2007

--

--

--

--

--

--

-S

eneg

al20

0422

3 73

120

0 00

0-

3.5

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2004

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020

000

020

000

012

.112

.113

5.2

135.

220

07-

--

-66

-0.

0-

--

--

-S

udan

2004

414

000

130

000

284

000

2.4

465

454

2 32

7 27

26.

4-

--

--

-20

0530

7 30

053

8 76

440

300

3.9

555

311

2 77

6 55

57.

5-

--

--

-20

0653

7 19

91

051

088

12 1

999.

259

5 48

62

977

432

7.9

288

894

32

283

167

--

--

2007

2 17

5 00

01

095

000

1 08

0 00

019

.564

9 93

73

846

738

8.4

267

719

92

690

000

1010

.632

.234

Syr

ian

Ara

b R

epub

lic20

04-

--

--

--

--

--

--

2005

--

--

--

--

--

--

-20

06-

--

--

--

--

--

--

2007

--

--

--

--

--

--

-Y

emen

2004

9 53

00

9 53

0-

36 9

05-

0.9

153

986

0-

--

-20

0516

3 00

015

0 00

013

000

1.5

56 6

45-

1.3

240

560

0-

--

-20

0620

0 00

020

0 00

00

3.2

75 0

42-

1.7

512

000

013

.90

153.

20

2007

--

--

--

--

--

--

-Eu

rope

Arm

enia

2004

00

0-

6 65

326

612

-47

--

--

-20

050

00

-47

41

896

-7

--

--

-20

060

00

-2

372

9 48

8-

0-

--

--

2007

--

--

--

--

--

--

-A

zerb

aija

n20

040

00

-56

253

213

761

7.3

386

0-

--

-20

050

00

-62

380

249

518

8.1

242

0-

--

-20

060

00

-47

161

188

643

6.1

143

071

.5-

86.6

-20

07-

--

--

--

--

--

--

Geo

rgia

2004

--

--

6 18

324

732

8.2

500

--

--

-20

05-

--

-13

272

53 0

8817

.830

0-

--

--

2006

--

--

13 2

0052

800

17.9

200

-24

1.5

-28

8.7

-20

07-

--

--

--

--

--

--

Kyr

gyzs

tan

2004

00

3 20

0-

8 00

040

000

1.0

930

--

--

2005

00

0-

8 00

040

000

1.0

210

0-

--

-20

060

020

000

-16

800

83 2

002.

132

00

85.1

086

.60

2007

--

--

--

--

--

--

-R

ussi

an F

eder

atio

n20

04-

--

--

--

--

--

--

2005

--

--

--

--

--

--

-20

06-

--

--

--

--

--

--

2007

--

--

--

--

--

--

-Ta

jikis

tan

2004

22 9

52-

22 9

52-

34 0

9323

8 65

13.

53

588

151

--

--

2005

19 9

93-

19 9

93-

40 2

3568

5 13

04.

02

309

81-

--

-20

0615

150

15 1

500

0.6

--

-1

344

28-

56.7

-65

.220

0726

438

26 4

38-

1.6

60 4

7594

1 66

75.

90

-0

-0

-


Ann

ex 5

: Ope

ratio

nal c

over

age

of in

sect

icid

e tr

eate

d ne

ts, i

ndoo

r res

idua

l spr

ayin

g an

d an

timal

aria

l tre

atm

ent,

2004

-200

7

WH

O re

gion

Cou

ntry

/are

aYe

ar

No.

of I

TN +

LLIN

sol

d or

deliv

ered

No.

of L

LIN

sol

dor

del

iver

edN

o. o

f ITN

sol

dor

del

iver

ed%

ITN

cove

rage

No.

of

hous

ehol

dssp

raye

d

No.

of

peop

lepr

otec

ted

by IR

S%

IRS

cove

rage

Any

1st

-line

trea

tmen

tco

urse

sde

liver

ed(in

clud

ing

AC

T)

AC

Ttr

eatm

ent

cour

ses

deliv

ered

% A

nyan

timal

aria

lco

vera

geto

tal

% A

CT

cove

rage

tota

l

% A

nyan

timal

aria

lco

vera

gepu

blic

% A

CT

cove

rage

publ

icTu

rkey

2004

--

--

50 1

8425

0 92

03.

541

993

8-

--

--

2005

--

--

41 3

7020

6 85

02.

985

400

4-

--

--

2006

--

--

62 6

6931

3 34

54.

31

139

577

-93

022.

8-

1239

83-

2007

--

--

--

--

--

--

-Tu

rkm

enis

tan

2004

--

--

--

-3

--

--

-20

05-

--

--

--

1-

--

--

2006

--

--

14 0

7070

350

-0

--

--

-20

07-

--

-17

368

86 8

40-

--

--

--

Uzb

ekis

tan

2004

-0

0-

18 2

0310

9 21

94.

766

0-

--

-20

05-

5 29

60

0.5

19 1

6911

5 04

64.

910

20

--

--

2006

-0

0-

20 3

5112

2 10

65.

176

072

.40

86.6

020

07-

--

--

--

--

--

--

Sout

h-Ea

st A

sia

Ban

glad

esh

2004

168

500

3 50

0-

0.0

--

--

--

--

-20

0530

5 00

05

000

-0.

0-

--

-37

754

--

--

2006

--

--

--

--

3544

8-

0.3

-8.

720

07-

--

--

--

--

--

--

Bhu

tan

2004

33 3

49-

33 3

49-

37 7

9717

9 11

741

.12

670

--

--

-20

0570

232

-70

232

-17

530

68 5

8218

.61

825

--

--

-20

06-

50 3

61-

21.0

36 0

7315

5 89

937

.61

868

905

--

--

2007

-50

298

-41

.421

954

75 0

3722

.691

839

150

819

22.3

89.8

3935

.715

865

Dem

ocra

tic P

eopl

e's

Rep

ublic

of K

orea

2004

20 0

00-

20 0

00-

--

-20

000

0-

--

--

2005

69 9

4720

000

49 9

471.

2-

--

100

000

--

--

-20

0612

0 00

020

000

100

000

2.4

--

-10

000

0-

69.7

-92

5.9

-20

07-

--

-1

567

5 20

50.

1-

--

--

-D

emoc

ratic

Rep

ublic

of T

imor

-Les

te20

04-

29 1

62-

5.8

--

--

--

--

-20

05-

0-

--

--

--

--

--

2006

--

--

--

--

--

--

-20

07-

--

--

--

--

--

--

Indi

a20

041

200

000

01

200

000

--

52 1

18 0

40-

191

536

3-

--

--

2005

2 72

0 00

00

2 72

0 00

0-

-62

935

123

-1

816

342

5770

0-

--

-20

061

495

000

01

495

000

--

69 4

57 9

13-

178

077

724

230

0-

--

-20

077

000

000

07

000

000

--

70 1

31 0

19-

147

656

255

000

01.

71.

370

.656

Indo

nesi

a20

0415

5 00

015

0 00

05

000

0.3

-74

9 50

0-

--

--

--

2005

--

--

--

--

--

--

-20

06-

2 00

0 00

0-

3.7

--

-25

000

0-

4.2

-12

.2-

2007

--

250

000

3.9

--

--

--

--

-M

yanm

ar20

0418

1 07

262

631

118

441

1.6

4 16

519

704

0.1

--

--

--

2005

222

886

14 2

9520

8 59

12.

04

733

31 8

550.

143

313

514

338

5-

--

-20

0653

8 43

684

546

453

890

3.6

2 95

112

360

0.0

100

071

039

208

55.

43

136.

974

.420

07-

--

--

--

--

--

--

Nep

al20

04-

--

-23

9 99

91

199

995

5.5

5072

40

--

--

2005

40 6

0040

600

-0.

421

7 19

21

085

960

4.9

8181

30

--

--

2006

113

017

113

017

-1.

422

2 56

31

112

817

4.9

7067

40

9.2

039

.50

2007

154

300

154

300

-2.

7-

--

--

--

--

Sri

Lank

a20

04-

-14

001

1.9

309

541

1 15

5 35

325

.33

720

--

--

-20

05-

223

000

4 50

08.

827

6 42

495

5 12

022

.51

640

--

--

-20

06-

100

000

010

.519

9 59

379

2 35

516

.259

1-

18.2

-86

.6-

2007

--

--

92 6

0935

8 10

47.

5-

--

--

-Th

aila

nd20

04-

-34

0 76

0-

322

875

1 15

6 67

84.

251

271

343

8-

--

-


Ann

ex 5

: Ope

ratio

nal c

over

age

of in

sect

icid

e tr

eate

d ne

ts, i

ndoo

r res

idua

l spr

ayin

g an

d an

timal

aria

l tre

atm

ent,

2004

-200

7

WH

O re

gion

Cou

ntry

/are

aYe

ar

No.

of I

TN +

LLIN

sol

d or

deliv

ered

No.

of L

LIN

sol

dor

del

iver

edN

o. o

f ITN

sol

dor

del

iver

ed%

ITN

cove

rage

No.

of

hous

ehol

dssp

raye

d

No.

of

peop

lepr

otec

ted

by IR

S%

IRS

cove

rage

Any

1st

-line

trea

tmen

tco

urse

sde

liver

ed(in

clud

ing

AC

T)

AC

Ttr

eatm

ent

cour

ses

deliv

ered

% A

nyan

timal

aria

lco

vera

geto

tal

% A

CT

cove

rage

tota

l

% A

nyan

timal

aria

lco

vera

gepu

blic

% A

CT

cove

rage

publ

icTh

aila

nd20

05-

-25

9 84

9-

300

074

834

949

3.9

623

820

246

--

--

2006

-9

000

185

290

1.0

207

156

695

854

2.7

6580

434

848

0.5

0.5

188.

121

3.7

2007

--

--

--

--

--

--

-W

este

rn P

acifi

cC

ambo

dia

2004

267

144

--

7.3

00

0.0

8999

384

421

--

--

2005

500

318

--

13.5

00

0.0

7778

275

082

--

--

2006

452

316

--

12.0

00

0.0

141

535

112

495

--

--

2007

456

581

120

598

-3.

10

00.

091

839

150

819

16.6

37.2

87.7

196.

2C

hina

2004

77 2

900

77 2

90-

--

-14

567

60

--

--

2005

61 3

540

61 3

54-

--

-10

010

60

--

--

2006

50 0

900

50 0

90-

--

-11

626

00

3.3

086

.60

2007

--

--

--

--

--

--

-La

o P

eopl

e's

Dem

ocra

tic R

epub

lic20

0425

9 60

0-

259

600

--

--

1620

016

200

--

--

2005

670

000

150

000

520

000

23.7

--

-77

760

7776

0-

--

-20

0632

0 00

0-

320

000

16.3

--

-14

064

014

064

0-

--

-20

0742

2 90

013

4 00

028

8 90

019

.6-

--

164

160

164

160

100.

510

4.7

682.

771

1.2

Mal

aysi

a20

0413

6 75

10

136

751

-66

561

211

696

7.4

615

40

--

--

2005

325

343

032

5 34

3-

25 6

3511

2 12

22.

85

569

0-

--

-20

0642

3 28

20

423

282

-26

874

111

623

2.9

529

40

0.5

086

.90

2007

--

--

--

--

--

--

-P

apua

New

Gui

nea

2004

8 41

8-

8 41

8-

--

-7

958

122

362

071

--

--

2005

228

421

228

421

-7.

5-

--

389

662

732

129

6-

--

-20

0646

1 23

146

1 23

1-

22.2

2 00

010

000

0.2

482

236

839

518

598

.211

.524

9.1

29.2

2007

53 5

0053

500

-23

.5-

24 6

99-

--

--

--

Phi

lippi

nes

2004

--

--

--

--

--

--

-20

05-

35 0

00-

0.2

--

--

--

--

-20

0641

1 78

147

000

362

775

2.1

--

--

--

--

-20

07-

196

000

200

000

2.3

--

--

--

--

-R

epub

lic o

f Kor

ea20

04-

--

--

--

207

0-

--

--

2005

--

--

--

-71

0-

--

--

2006

--

--

--

-1

045

-16

.1-

33.1

-20

07-

--

--

--

--

--

--

Sol

omon

Isla

nds

2004

15 7

12-

-6.

918

070

99 0

9919

.7-

--

--

-20

0548

665

--

20.7

11 1

3763

364

11.9

--

--

--

2006

59 9

0453

162

6 74

224

.916

849

93 4

8717

.5-

--

--

-20

07-

--

--

--

--

--

--

Van

uatu

2004

14 1

320

14 1

32-

--

-18

103

70

--

--

2005

9 26

59

265

08.

6-

--

135

705

0-

--

-20

0622

834

22 8

340

29.2

--

-13

100

00

140.

70

535.

50

2007

--

--

--

--

--

--

-V

iet N

am20

04-

--

-1

885

000

-9.

91

624

000

183

333

--

--

2005

1 20

1 00

01

000

1 20

0 00

02.

82

000

000

-10

.31

600

000

161

667

--

--

2006

800

000

-80

0 00

01.

91

900

000

-9.

71

600

000

231

508

76.4

1415

16.8

277.

420

07-

--

--

--

--

--

--

ITN

= In

sect

icid

e-tre

ated

net

sLL

IN =

Lon

g la

stin

g in

sect

icid

e-tre

ated

net

sA

CT

= A

rtem

isin

in-b

ased

com

bina

tion

ther

apy

ANNEX 6

a. Household surveys of mosquito net ownership and usage, 2000–2007

b. Household surveys of antimalarial treatment, 2001–2007


Ann

ex 6

A: H

ouse

hold

sur

veys

of m

osqu

ito n

ets

owne

rshi

p an

d us

age,

200

1-20

07

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Year

Sour

ceSu

bgro

up

% o

f HH

with

1an

y ne

t

% o

f HH

with

1 ev

er tr

eate

dne

t

% o

f HH

with

1IT

N

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

any

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

ever

trea

ted

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

an

ITN

% o

f pre

gnan

tw

omen

who

slep

t und

eran

y ne

t

% o

f pre

gnan

tw

omen

who

slep

t und

erev

er tr

eate

dne

t

% o

f pre

gnan

tw

omen

who

slep

t und

er a

nIT

NA

fric

aA

ngol

a20

06M

IS 2

006-

07To

tal

33-

2821

-18

--

2220

06M

IS 2

006-

07U

rban

34-

2919

-17

--

1520

06M

IS 2

006-

07R

ural

31-

2622

-19

--

26B

enin

2001

DH

S 2

001-

02To

tal

40-

-32

-7

--

-20

01D

HS

200

1-02

Urb

an49

--

43-

14-

--

2001

DH

S 2

001-

02R

ural

35-

-27

-4

--

-20

06D

HS

200

6To

tal

56-

2547

-20

--

2020

06D

HS

200

6U

rban

66-

2955

-25

--

2520

06D

HS

200

6R

ural

50-

2142

-18

--

17B

urki

na F

aso

2003

DH

S 2

003

Tota

l40

156

207

224

83

2003

DH

S 2

003

Urb

an46

2415

2311

625

138

2003

DH

S 2

003

Rur

al39

123

196

124

82

2006

MIC

S 2

006

Tota

l52

-23

18-

10-

--

2006

MIC

S 2

006

Urb

an65

-45

33-

24-

--

2006

MIC

S 2

006

Rur

al47

-15

14-

6-

--

Bur

undi

2000

MIC

S 2

000

Tota

l-

--

3-

1-

--

2000

MIC

S 2

000

Urb

an-

--

28-

15-

--

2000

MIC

S 2

000

Rur

al-

--

1-

0-

--

2005

MIC

S 2

005

Tota

l13

-8

13-

8-

--

2005

MIC

S 2

005

Urb

an49

-34

51-

40-

--

2005

MIC

S 2

005

Rur

al11

-6

12-

7-

--

Cam

eroo

n20

00M

ICS

200

0To

tal

--

-11

-1

--

-20

00M

ICS

200

0U

rban

--

-18

-3

--

-20

00M

ICS

200

0R

ural

--

-9

-1

--

-20

04D

HS

200

4To

tal

202

112

11

122

120

04D

HS

200

4U

rban

243

217

22

153

320

04D

HS

200

4R

ural

171

17

10

100

020

06M

ICS

200

6To

tal

32-

2027

-13

--

-20

06M

ICS

200

6U

rban

33-

2032

-14

--

-20

06M

ICS

200

6R

ural

30-

2022

-12

--

-C

entra

l Afri

can

Rep

ublic

2000

MIC

S 2

000

Tota

l-

--

31-

2-

--

2000

MIC

S 2

000

Urb

an-

--

48-

2-

--

2000

MIC

S 2

000

Rur

al-

--

20-

1-

--

2006

MIC

S 2

006

Tota

l36

-17

33-

15-

--

2006

MIC

S 2

006

Urb

an54

-27

52-

24-

--

2006

MIC

S 2

006

Rur

al26

-12

22-

10-

--

Cha

d20

00M

ICS

200

0To

tal

--

-27

-1

--

-20

00M

ICS

200

0U

rban

--

-58

-1

--

-20

00M

ICS

200

0R

ural

--

-19

-0

--

-20

04D

HS

200

4To

tal

64-

--

--

--

-20

04D

HS

200

4U

rban

77-

--

--

--

-20

04D

HS

200

4R

ural

61-

--

--

--

-C

omor

os20

00M

ICS

200

0To

tal

--

-36

-9

--

-20

00M

ICS

200

0U

rban

--

-57

-17

--

-20

00M

ICS

200

0R

ural

--

-31

-7

--

-C

ongo

2005

DH

S 2

005

Tota

l76

98

687

664

54

2005

DH

S 2

005

Urb

an82

98

777

670

44

2005

DH

S 2

005

Rur

al68

98

607

659

55

Côt

e d'

Ivoi

re20

00M

ICS

200

0To

tal

--

-10

-1

--

-20

00M

ICS

200

0U

rban

--

-12

-2

--

-


Ann

ex 6

A: H

ouse

hold

sur

veys

of m

osqu

ito n

ets

owne

rshi

p an

d us

age,

200

1-20

07

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Year

Sour

ceSu

bgro

up

% o

f HH

with

1an

y ne

t

% o

f HH

with

1 ev

er tr

eate

dne

t

% o

f HH

with

1IT

N

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nde r

any

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nde r

ever

trea

ted

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

an

ITN

% o

f pre

gnan

tw

omen

who

slep

t und

eran

y ne

t

% o

f pre

gnan

tw

omen

who

slep

t und

e rev

er tr

eate

dne

t

% o

f pre

gnan

tw

omen

who

slep

t und

er a

nIT

N20

00M

ICS

200

0R

ural

--

-8

-1

--

-20

05A

IS 2

005

Tota

l20

-3

--

--

--

2005

AIS

200

5U

rban

17-

3-

--

--

-20

05A

IS 2

005

Rur

al23

-2

--

--

--

2006

MIC

S 2

006

Tota

l27

-6

17-

6-

--

2006

MIC

S 2

006

Urb

an22

-6

16-

8-

--

2006

MIC

S 2

006

Rur

al31

-6

18-

4-

--

Dem

ocra

tic R

epub

lic o

f the

Con

go20

01M

ICS

200

1To

tal

--

-12

-1

--

-20

01M

ICS

200

1U

rban

--

-15

-2

--

-20

01M

ICS

200

1R

ural

--

-10

-0

--

-E

quat

oria

l Gui

nea

2000

MIC

S 2

000

Tota

l-

--

15-

1-

--

2000

MIC

S 2

000

Urb

an-

--

30-

3-

--

2000

MIC

S 2

000

Rur

al-

--

10-

0*-

--

Erit

rea

2002

DH

S 2

002

Tota

l34

--

12-

4*-

-3*

2002

DH

S 2

002

Urb

an28

--

14-

5*-

-5*

2002

DH

S 2

002

Rur

al37

--

11-

4-

-2

Eth

iopi

a20

00D

HS

200

0To

tal

1-

0-

--

--

-20

00D

HS

200

0U

rban

3-

0-

--

--

-20

00D

HS

200

0R

ural

1-

0-

--

--

-20

05D

HS

200

5To

tal

64

32

22

21

120

05D

HS

200

5U

rban

118

59

74

1110

620

05D

HS

200

5R

ural

5-

32*

-1

--

120

07M

IS 2

007

Tota

l56

5453

35*

3333

*37

3535

2007

MIS

200

7ªTo

tal

69-

65-

-42

--

4320

07M

IS 2

007

Urb

an41

4040

4137

3637

3434

2007

MIS

200

7R

ural

5957

5634

3333

3736

34G

abon

2000

DH

S 2

000

Tota

l-

--

--

--

--

Gam

bia

2000

MIC

S 2

000

Tota

l-

--

42-

15-

--

2000

MIC

S 2

000

Urb

an-

--

36-

7-

--

2000

MIC

S 2

000

Rur

al-

--

46-

19-

--

2006

MIC

S 2

006

Tota

l59

-50

63-

49-

--

2006

MIC

S 2

006

Urb

an49

-13

55-

38-

--

2006

MIC

S 2

006

Rur

al70

-38

68-

55-

--

Gha

na20

03D

HS

200

3To

tal

18-

315

-4

--

320

03D

HS

200

3U

rban

10-

29

-4

--

220

03D

HS

200

3R

ural

24-

418

-4

--

320

06M

ICS

200

6To

tal

30-

1933

-22

--

-20

06M

ICS

200

6U

rban

21-

1522

-16

--

-20

06M

ICS

200

6R

ural

37-

2238

-25

--

-G

uine

a20

05D

HS

200

5To

tal

272

412

11

131

120

05D

HS

200

5U

rban

282

616

13

111

420

05D

HS

200

5R

ural

272

311

11

131

1G

uine

a-B

issa

u20

00M

ICS

200

0To

tal

--

-67

-7

--

-20

00M

ICS

200

0U

rban

--

-75

-19

--

-20

00M

ICS

200

0R

ural

--

-64

-3

--

-20

06M

ICS

200

6To

tal

79-

4473

-39

--

-20

06M

ICS

200

6U

rban

82-

3580

-32

--

-20

06M

ICS

200

6R

ural

78-

4971

-42

--

-K

enya

2000

MIC

S 2

000

Tota

l-

--

16-

3-

--

2000

MIC

S 2

000

Urb

an-

--

35-

4-

--


Ann

ex 6

A: H

ouse

hold

sur

veys

of m

osqu

ito n

ets

owne

rshi

p an

d us

age,

200

1-20

07

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Year

Sour

ceSu

bgro

up

% o

f HH

with

1an

y ne

t

% o

f HH

with

1 ev

er tr

eate

dne

t

% o

f HH

with

1IT

N

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

any

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

ever

trea

ted

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

an

ITN

% o

f pre

gnan

tw

omen

who

slep

t und

eran

y ne

t

% o

f pre

gnan

tw

omen

who

slep

t und

erev

er tr

eate

dne

t

% o

f pre

gnan

tw

omen

who

slep

t und

er a

nIT

N20

00M

ICS

200

0R

ural

--

-10

-3

--

-20

03D

HS

200

3To

tal

229

815

-6

13-

520

03D

HS

200

3U

rban

3815

1433

-12

26-

720

03D

HS

200

3R

ural

177

611

-5

10-

5Li

beria

2005

MIS

200

5To

tal

18-

611

-3

--

-M

adag

asca

r20

00M

ICS

200

0To

tal

--

-30

-0

--

-20

00M

ICS

200

0U

rban

--

-32

-0

--

-20

00M

ICS

200

0R

ural

--

-30

-0

--

-20

03D

HS

200

3-04

Tota

l39

--

--

--

--

2003

DH

S 2

003-

04U

rban

44-

--

--

--

-20

03D

HS

200

3-04

Rur

al37

--

--

--

--

Mal

awi

2000

DH

S 2

000

Tota

l13

--

8-

3-

--

2000

DH

S 2

000

Urb

an32

--

21-

12-

--

2000

DH

S 2

000

Rur

al10

--

6-

2-

--

2004

DH

S 2

004

Tota

l42

3427

2018

1519

1815

2004

DH

S 2

004

Urb

an56

4841

3935

3038

3630

2004

DH

S 2

004

Rur

al39

3225

1715

1216

1512

2006

MIC

S 2

006

Tota

l50

-36

29-

23-

--

2006

MIC

S 2

006

Urb

an72

-56

52-

43-

--

2006

MIC

S 2

006

Rur

al47

-34

*26

-21

*-

--

Mal

i20

01D

HS

200

1To

tal

54-

--

--

--

-20

01D

HS

200

1U

rban

58-

--

--

--

-20

01D

HS

200

1R

ural

53-

--

--

--

-20

06D

HS

200

6To

tal

69-

5041

-27

--

2920

06D

HS

200

6U

rban

72-

5441

-29

--

2220

06D

HS

200

6R

ural

68-

4841

-26

--

31M

aurit

ania

2000

DH

S 2

000-

01To

tal

56-

--

--

--

-20

00D

HS

200

0-01

Urb

an40

--

--

--

--

2000

DH

S 2

000-

01R

ural

67-

--

--

--

-20

03D

HS

200

3-04

Tota

l56

-1

31-

2-

--

2003

DH

S 2

003-

04U

rban

43-

126

-2

--

-20

03D

HS

200

3-04

Rur

al66

-1

35-

2-

--

Moz

ambi

que

2003

DH

S 2

003

(Nat

iona

l rep

ort)

Tota

l-

--

10-

--

--

2003

DH

S 2

003

(Nat

iona

l rep

ort)

Urb

an-

--

16-

--

--

2003

DH

S 2

003

(Nat

iona

l rep

ort)

Rur

al-

--

7-

--

--

Nam

ibia

2000

DH

S 2

000

Tota

l13

--

7-

--

--

2000

DH

S 2

000

Urb

an11

--

5-

--

--

2000

DH

S 2

000

Rur

al15

--

8-

--

--

Nig

er20

00M

ICS

200

0To

tal

--

-17

-1

--

-20

00M

ICS

200

0U

rban

--

-36

-4

--

-20

00M

ICS

200

0R

ural

--

-14

-1

--

-20

06C

DC

-MM

P N

atio

nal S

urve

yTo

tal

--

65-

-56

--

4820

06D

HS

200

6To

tal

6969

4315

157

1313

720

06D

HS

200

6U

rban

7675

3732

3215

3030

1520

06D

HS

200

6R

ural

6867

4412

126

1111

5N

iger

ia20

03D

HS

200

3To

tal

123

26

-1

5-

120

03D

HS

200

3U

rban

52

14

-1

3-

020

03D

HS

200

3R

ural

164

37

-1

6-

2R

wan

da20

00D

HS

200

0To

tal

7-

-6

-4

--

-20

00M

ICS

200

0To

tal

--

-6

-5

--

-


Ann

ex 6

A: H

ouse

hold

sur

veys

of m

osqu

ito n

ets

owne

rshi

p an

d us

age,

200

1-20

07

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Year

Sour

ceSu

bgro

up

% o

f HH

with

1an

y ne

t

% o

f HH

with

1 ev

er tr

eate

dne

t

% o

f HH

with

1IT

N

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nde r

any

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nde r

ever

trea

ted

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

an

ITN

% o

f pre

gnan

tw

omen

who

slep

t und

eran

y ne

t

% o

f pre

gnan

tw

omen

who

slep

t und

e rev

er tr

eate

dne

t

% o

f pre

gnan

tw

omen

who

slep

t und

er a

nIT

N20

00D

HS

200

0U

rban

30-

-27

-21

--

-20

00M

ICS

200

0U

rban

--

-28

-24

--

-20

00D

HS

200

0R

ural

3-

-2

-1

--

-20

00M

ICS

200

0R

ural

--

-2

-2

--

-20

05D

HS

200

5To

tal

1818

1515

1513

2020

1720

05D

HS

200

5U

rban

4040

3232

3125

3535

2920

05D

HS

200

5R

ural

1414

1213

1311

1818

16S

ao T

ome

and

Prin

cipe

2000

MIC

S 2

000

Tota

l-

--

43-

23-

--

2000

MIC

S 2

000

Urb

an-

--

60-

32-

--

2000

MIC

S 2

000

Rur

al-

--

27-

14-

--

2006

MIC

S 2

006

Tota

l49

-36

53-

42-

--

2006

MIC

S 2

006

Urb

an58

-44

62-

51-

--

2006

MIC

S 2

006

Rur

al37

-25

*41

-29

*-

--

Sen

egal

2000

MIC

S 2

000

Tota

l-

--

15-

2*-

--

2000

MIC

S 2

000

Urb

an-

--

13-

2*-

--

2000

MIC

S 2

000

Rur

al-

--

16-

2-

--

2005

DH

S 2

005

Tota

l38

2720

1410

714

109

2005

DH

S 2

005

Urb

an32

2518

1410

715

1110

2005

DH

S 2

005

Rur

al44

2922

149

714

108

2006

MIS

200

6To

tal

57-

3628

-16

--

1720

06M

IS 2

006

Urb

an47

-34

23-

15-

-12

2006

MIS

200

6R

ural

65-

3830

-17

--

20S

ierr

a Le

one

2000

MIC

S 2

000

Tota

l-

--

15-

2-

--

2000

MIC

S 2

000

Urb

an-

--

13-

4-

--

2000

MIC

S 2

000

Rur

al-

--

16-

1-

--

2005

MIC

S 2

005

Tota

l20

-5

20-

5-

--

2005

MIC

S 2

005

Urb

an15

-5

15-

5-

--

2005

MIC

S 2

005

Rur

al22

-5

22-

5-

--

Sw

azila

nd20

00M

ICS

200

0To

tal

--

-0

-0

--

-20

00M

ICS

200

0U

rban

--

-0

-0

--

-20

00M

ICS

200

0R

ural

--

-0

-0

--

-To

go20

00M

ICS

200

0To

tal

--

-15

-2

--

-20

00M

ICS

200

0U

rban

--

-19

-4

--

-20

00M

ICS

200

0R

ural

--

-13

-1

--

-20

05C

DC

-MM

P N

atio

nal S

urve

yTo

tal

--

63-

-44

--

-20

06M

ICS

200

6To

tal

46-

4041

-38

--

-20

06M

ICS

200

6U

rban

44-

3739

-36

--

-20

06M

ICS

200

6R

ural

47-

4242

-40

--

-U

gand

a20

00D

HS

200

0-01

Tota

l13

--

7-

0-

-1

2000

DH

S 2

000-

01U

rban

33-

-21

-1

--

020

00D

HS

200

0-01

Rur

al9

--

6-

0-

-1

2004

AIS

200

4-05

Tota

l26

--

--

--

--

2004

AIS

200

4-05

Urb

an60

--

--

--

--

2004

AIS

200

4-05

Rur

al20

--

--

--

--

2006

DH

S 2

006

Tota

l34

2116

2213

1024

1310

2006

DH

S 2

006

Urb

an61

3626

4929

2149

2723

2006

DH

S 2

006

Rur

al29

1914

1811

822

129

Uni

ted

Rep

ublic

of T

anza

nia

1999

DH

S 1

999

Tota

l30

-1

21-

2-

--

1999

DH

S 1

999

Urb

an57

--

48-

5-

--

1999

DH

S 1

999

Rur

al21

--

13-

1-

--


Ann

ex 6

A: H

ouse

hold

sur

veys

of m

osqu

ito n

ets

owne

rshi

p an

d us

age,

200

1-20

07

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Year

Sour

ceSu

bgro

up

% o

f HH

with

1an

y ne

t

% o

f HH

with

1 ev

er tr

eate

dne

t

% o

f HH

with

1IT

N

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

any

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

ever

trea

ted

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

an

ITN

% o

f pre

gnan

tw

omen

who

slep

t und

eran

y ne

t

% o

f pre

gnan

tw

omen

who

slep

t und

erev

er tr

eate

dne

t

% o

f pre

gnan

tw

omen

who

slep

t und

er a

nIT

N20

04D

HS

200

4-05

Tota

l46

2923

3120

16-

2016

2004

DH

S 2

004-

05U

rban

7454

4763

4640

-47

3920

04D

HS

200

4-05

Rur

al36

2014

2413

10-

1310

Zam

bia

1999

MIC

S 1

999

Tota

l-

--

6-

1-

--

1999

MIC

S 1

999

Urb

an-

--

9-

2-

--

1999

MIC

S 1

999

Rur

al-

--

5-

1-

--

2001

DH

S 2

001-

02To

tal

2716

1216

-7

17-

920

01D

HS

200

1-02

Urb

an35

1814

22-

923

-11

2001

DH

S 2

001-

02R

ural

2314

1114

-7

15-

820

06M

IS 2

006

Tota

l50

-44

27-

23-

-24

2006

MIS

200

6U

rban

51-

4531

-26

--

1820

06M

IS 2

006

Rur

al50

-44

24-

21-

-27

Zim

babw

e19

99D

HS

199

9To

tal

10-

-3

--

--

-20

05D

HS

200

5-06

Tota

l20

109

74

37

33

2005

DH

S 2

005-

06U

rban

3414

1116

75

167

620

05D

HS

200

5-06

Rur

al13

87

33

24

22

Am

eric

asC

olom

bia

2000

DH

S 2

000

Tota

l31

-3

24-

--

--

2000

DH

S 2

000

Urb

an31

-3

23-

--

--

2000

DH

S 2

000

Rur

al32

-4

26-

--

--

Gua

tem

ala

1999

MIC

S 1

999

Tota

l-

--

6-

1-

--

Hai

ti20

00D

HS

200

0To

tal

--

--

--

--

-20

00D

HS

200

0U

rban

--

--

--

--

-20

00D

HS

200

0R

ural

--

--

--

--

-20

05D

HS

200

5To

tal

6-

--

--

--

-20

05D

HS

200

5U

rban

11-

--

--

--

-20

05D

HS

200

5R

ural

4-

--

--

--

-H

ondu

ras

2005

DH

S 2

005-

06To

tal

--

--

--

--

-20

05D

HS

200

5-06

Urb

an-

--

--

--

--

2005

DH

S 2

005-

06R

ural

--

--

--

--

-N

icar

agua

2001

DH

S 2

001

Tota

l42

--

--

--

--

2001

DH

S 2

001

Urb

an46

--

--

--

--

2001

DH

S 2

001

Rur

al37

--

--

--

--

Sur

inam

e20

00M

ICS

200

0To

tal

--

-77

-3

--

-Ea

ster

n M

edite

rran

ean

Djib

outi

2006

MIC

S 2

006

Tota

l26

-18

9-

1-

--

2006

MIC

S 2

006

Urb

an26

-18

9-

1-

--

2006

MIC

S 2

006

Rur

al22

-12

8-

1-

--

Iraq

2000

MIC

S 2

000

Tota

l-

--

7-

0-

--

2000

MIC

S 2

000

Urb

an-

--

7-

0-

--

2000

MIC

S 2

000

Rur

al-

--

8-

0-

--

Som

alia

2006

MIC

S 2

006

Tota

l22

-12

18-

9-

--

2006

MIC

S 2

006

Urb

an27

-16

25-

15-

--

2006

MIC

S 2

006

Rur

al20

-10

14-

6-

--

Sud

an20

00M

ICS

200

0To

tal

--

-23

-0

--

-20

00M

ICS

200

0U

rban

--

-26

-1

--

-20

00M

ICS

200

0R

ural

--

-21

-0

--

-20

06N

atio

nal H

ouse

hold

Sur

vey

Tota

l-

-18

--

28-

--

Euro

peA

zerb

aija

n20

00M

ICS

200

0To

tal

--

-12

-1

--

-20

00M

ICS

200

0U

rban

--

-7

-1

--

-20

00M

ICS

200

0R

ural

--

-18

-2

--

-


Ann

ex 6

A: H

ouse

hold

sur

veys

of m

osqu

ito n

ets

owne

rshi

p an

d us

age,

200

1-20

07

WH

O re

gion

/sub

regi

onC

ount

ry/a

rea

Year

Sour

ceSu

bgro

up

% o

f HH

with

1an

y ne

t

% o

f HH

with

1 ev

er tr

eate

dne

t

% o

f HH

with

1IT

N

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nde r

any

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nde r

ever

trea

ted

net

% o

f chi

ldre

n<5

yea

rs w

hosl

ept u

nder

an

ITN

% o

f pre

gnan

tw

omen

who

slep

t und

eran

y ne

t

% o

f pre

gnan

tw

omen

who

slep

t und

e rev

er tr

eate

dne

t

% o

f pre

gnan

tw

omen

who

slep

t und

er a

nIT

NTa

jikis

tan

2005

MIC

S 2

005

Tota

l5

-2

2-

1-

--

2005

MIC

S 2

005

Urb

an2

-0

1-

0-

--

2005

MIC

S 2

005

Rur

al6

-3

2-

2-

--

Sout

h-Ea

st A

sia

Dem

ocra

tic R

epub

lic o

f Tim

or-L

este

2002

MIC

S 2

002

Tota

l-

--

48-

8-

--

2002

MIC

S 2

002

Urb

an-

--

75-

12-

--

2002

MIC

S 2

002

Rur

al-

--

39-

6-

--

Indi

a20

00M

ICS

200

0To

tal

--

--

--

--

-20

00M

ICS

200

0U

rban

--

--

--

--

-20

00M

ICS

200

0R

ural

--

--

--

--

-20

05D

HS

200

5-06

Tota

l36

--

--

--

--

2005

DH

S 2

005-

06U

rban

32-

--

--

--

-20

05D

HS

200

5-06

Rur

al37

--

--

--

--

Indo

nesi

a20

00M

ICS

200

0To

tal

--

-32

-0

--

-20

00M

ICS

200

0U

rban

--

-23

-0

--

-20

00M

ICS

200

0R

ural

--

-38

-0

--

-20

02D

HS

200

2-03

Tota

l-

--

--

--

--

2002

DH

S 2

002-

03U

rban

--

--

--

--

-20

02D

HS

200

2-03

Rur

al-

--

--

--

--

Wes

tern

Pac

ific

Cam

bodi

a20

05D

HS

200

5To

tal

9610

588

94

869

420

05D

HS

200

5U

rban

957

282

52

825

220

05D

HS

200

5R

ural

9610

589

105

879

5La

o P

eopl

e's

Dem

ocra

tic R

epub

lic20

00M

ICS

200

0To

tal

--

-82

-18

--

-20

00M

ICS

200

0U

rban

--

-97

-11

--

-20

00M

ICS

200

0R

ural

--

-78

-20

--

-V

iet N

am20

00M

ICS

200

0To

tal

--

-96

-16

--

-20

00M

ICS

200

0U

rban

--

-94

-4

--

-20

00M

ICS

200

0R

ural

--

-96

-19

--

-20

05A

IS 2

005

Tota

l97

-12

95-

13-

-15

2005

AIS

200

5U

rban

90-

589

-3

--

120

05A

IS 2

005

Rur

al99

-14

96-

15-

-19

2006

MIC

S 2

006

Tota

l97

-19

94-

5-

--

2006

MIC

S 2

006

Urb

an92

-5

88-

12-

--

2006

MIC

S 2

006

Rur

al99

-23

95-

3-

--

AIS

= A

IDS

Indi

cato

r Sur

vey.

CD

C-M

MP

= C

ente

rs fo

r Dis

ease

Con

trol a

nd P

reve

ntio

n - M

alar

ia M

easl

es P

artn

ersh

ip.

DH

S =

Dem

ogra

phic

and

Hea

lth S

urve

y.M

ICS

= M

ultip

le In

dica

tor C

lust

er S

urve

y.M

IS =

Mal

aria

Indi

cato

r Sur

vey.

*Dat

a up

date

d by

DH

S s

ince

the

orig

inal

pub

licat

ion.

ªPer

cent

ages

cal

cula

ted

usin

g th

e po

pula

tion

at ri

sk.


Ann

ex 6

B: H

ouse

hold

sur

veys

of a

ntim

alar

ial t

reat

men

t, 20

01-2

007

WH

O re

gion

Cou

ntry

/are

aYe

arSo

urce

Subg

roup

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok S

P/ F

ansi

dar

sam

e or

nex

t day

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok c

hlor

oqui

nesa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

sam

e or

nex

t day

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gssa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok S

P/Fa

nsid

ar

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

okch

loro

quin

e

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gs

% o

f pre

gnan

tw

omen

who

took

at le

ast 2

dos

esSP

/Fan

sida

r

Afr

ica

Ang

ola

2006

MIS

200

6-07

Tota

l-

--

180

142

293

2006

MIS

200

6-07

Urb

an-

--

27-

--

384

2006

MIS

200

6-07

Rur

al-

--

12-

--

231

Ben

in20

01D

HS

200

1-02

Tota

l-

--

-1

59-

60*

-

2001

DH

S 2

001-

02U

rban

--

--

--

-62

*-

2001

DH

S 2

001-

02R

ural

--

--

--

-60

*-

2006

DH

S 2

006

Tota

l-

--

421

490

543

2006

DH

S 2

006

Urb

an-

--

48-

--

573

2006

DH

S 2

006

Rur

al-

--

39-

--

533

Bur

kina

Fas

o20

03D

HS

200

3To

tal

044

-45

048

-50

-

2003

DH

S 2

003

Urb

an1

50-

561

52-

60-

2003

DH

S 2

003

Rur

al0

43-

440

47-

48-

2006

MIC

S 2

006

Tota

l-

--

410

46-

481

2006

MIC

S 2

006

Urb

an-

--

61-

--

702

2006

MIC

S 2

006

Rur

al-

--

36-

--

421

Bur

undi

2000

MIC

S 2

000

Tota

l-

--

-2

23-

31-

2000

MIC

S 2

000

Urb

an-

--

--

--

42-

2000

MIC

S 2

000

Rur

al-

--

--

--

31-

2005

MIC

S 2

005

Tota

l-

--

192

23

30-

2005

MIC

S 2

005

Urb

an-

--

22-

--

28-

2005

MIC

S 2

005

Rur

al-

--

19-

--

30-

Cam

eroo

n20

00M

ICS

200

0To

tal

--

--

148

-66

-

2000

MIC

S 2

000

Urb

an-

--

--

--

71-

2000

MIC

S 2

000

Rur

al-

--

--

--

64-

2004

DH

S 2

004

Tota

l1

14-

401

20-

66-

2004

DH

S 2

004

Urb

an1

13-

461

17-

72-

2004

DH

S 2

004

Rur

al1

15-

341

22-

61-

2006

MIC

S 2

006

Tota

l-

--

382

82

586

2006

MIC

S 2

006

Urb

an-

--

53-

--

698

2006

MIC

S 2

006

Rur

al-

--

29-

--

504

Cen

tral A

frica

n R

epub

lic20

00M

ICS

200

0To

tal

--

--

066

-69

-

2000

MIC

S 2

000

Urb

an-

--

--

--

76-

2000

MIC

S 2

000

Rur

al-

--

--

--

65-

2006

MIC

S 2

006

Tota

l-

--

424

293

579

2006

MIC

S 2

006

Urb

an-

--

48-

--

6815

2006

MIC

S 2

006

Rur

al-

--

36-

--

475

Cha

d20

00M

ICS

200

0To

tal

--

--

131

-32

-

2000

MIC

S 2

000

Urb

an-

--

--

--

41-

2000

MIC

S 2

000

Rur

al-

--

--

--

30-

2004

DH

S 2

004

Tota

l-

--

--

--

--

2004

DH

S 2

004

Urb

an-

--

--

--

--

2004

DH

S 2

004

Rur

al-

--

--

--

--

Com

oros

2000

MIC

S 2

000

Tota

l-

--

-4

62-

63-

2000

MIC

S 2

000

Urb

an-

--

--

--

65-

2000

MIC

S 2

000

Rur

al-

--

--

--

62-

Con

go20

05D

HS

200

5To

tal

120

422

124

748

-

2005

DH

S 2

005

Urb

an1

123

201

1910

42-

2005

DH

S 2

005

Rur

al0

171

231

284

52-

Côt

e d'

Ivoi

re20

00M

ICS

200

0To

tal

--

--

356

-58

-

2000

MIC

S 2

000

Urb

an-

--

--

--

69-

2000

MIC

S 2

000

Rur

al-

--

--

--

50-


Ann

ex 6

B: H

ouse

hold

sur

veys

of a

ntim

alar

ial t

reat

men

t, 20

01-2

007

WH

O re

gion

Cou

ntry

/are

aYe

arSo

urce

Subg

roup

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok S

P/ F

ansi

dar

sam

e or

nex

t day

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok c

hlor

oqui

nesa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

sam

e or

nex

t day

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gssa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok S

P/Fa

nsid

ar

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

okch

loro

quin

e

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gs

% o

f pre

gnan

tw

omen

who

took

at le

ast 2

dos

esSP

/Fan

sida

r

2005

AIS

200

5To

tal

--

--

--

--

-

2005

AIS

200

5U

rban

--

--

--

--

-

2005

AIS

200

5R

ural

--

--

--

--

-

2006

MIC

S 2

006

Tota

l-

--

262

313

36*

8

2006

MIC

S 2

006

Urb

an-

--

32-

--

4510

2006

MIC

S 2

006

Rur

al-

--

23-

--

327

Dem

ocra

tic R

epub

lic o

f the

Con

go20

01M

ICS

200

1To

tal

--

--

145

-52

-

2001

MIC

S 2

001

Urb

an-

--

--

--

63-

2001

MIC

S 2

001

Rur

al-

--

--

--

47-

Equ

ator

ial G

uine

a20

00M

ICS

200

0To

tal

--

--

-41

-49

-

2000

MIC

S 2

000

Urb

an-

--

--

--

55-

2000

MIC

S 2

000

Rur

al-

--

--

--

43-

Erit

rea

2002

DH

S 2

002

Tota

l-

--

21

2-

4-

2002

DH

S 2

002

Urb

an-

--

2-

--

4-

2002

DH

S 2

002

Rur

al-

--

2-

--

4-

Eth

iopi

a20

00D

HS

200

0To

tal

--

--

12

-3

-

2000

DH

S 2

000

Urb

an-

--

--

--

--

2000

DH

S 2

000

Rur

al-

--

--

--

--

2005

DH

S 2

005

Tota

l0

0-

11

1-

3-

2005

DH

S 2

005

Urb

an0

1-

21

3-

4-

2005

DH

S 2

005

Rur

al-

--

1*-

--

3*-

2007

MIS

200

7To

tal

--

-4*

--

-10

*-

2007

MIS

200

7U

rban

--

-4*

--

-13

*-

2007

MIS

200

7R

ural

--

-4

--

-9

-

2007

MIS

200

7ªTo

tal

--

-5

--

-12

-

Gab

on20

00D

HS

200

0To

tal

--

--

-39

--

-

Gam

bia

2000

MIC

S 2

000

Tota

l-

--

-3

55-

55-

2000

MIC

S 2

000

Urb

an-

--

--

--

58-

2000

MIC

S 2

000

Rur

al-

--

--

--

54-

2006

MIC

S 2

006

Tota

l-

--

5213

*58

*0

6333

2006

MIC

S 2

006

Urb

an-

--

54-

--

5931

2006

MIC

S 2

006

Rur

al-

--

52-

--

6534

Gha

na20

03D

HS

200

3To

tal

--

-44

059

-63

1

2003

DH

S 2

003

Urb

an-

--

49-

--

651

2003

DH

S 2

003

Rur

al-

--

41-

--

611

2006

MIC

S 2

006

Tota

l-

--

481

424

61*

27

2006

MIC

S 2

006

Urb

an-

--

58-

--

6935

2006

MIC

S 2

006

Rur

al-

--

44-

--

5724

Gui

nea

2005

DH

S 2

005

Tota

l0

20-

311

270

443

2005

DH

S 2

005

Urb

an0

26-

401

3361

528

2005

DH

S 2

005

Rur

al0

19-

281

2635

421

Gui

nea-

Bis

sau

2000

MIC

S 2

000

Tota

l-

--

-3

58-

58-

2000

MIC

S 2

000

Urb

an-

--

--

--

72-

2000

MIC

S 2

000

Rur

al-

--

--

--

52-

2006

MIC

S 2

006

Tota

l-

--

272

41-

467

2006

MIC

S 2

006

Urb

an-

--

47-

--

609

2006

MIC

S 2

006

Rur

al-

--

18-

--

397

Ken

ya20

00M

ICS

200

0To

tal

--

--

2644

-65

-

2000

MIC

S 2

000

Urb

an-

--

--

--

64-

2000

MIC

S 2

000

Rur

al-

--

--

--

65-

2003

DH

S 2

003

Tota

l6

--

1111

3-

274


Ann

ex 6

B: H

ouse

hold

sur

veys

of a

ntim

alar

ial t

reat

men

t, 20

01-2

007

WH

O re

gion

Cou

ntry

/are

aYe

arSo

urce

Subg

roup

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok S

P/ F

ansi

dar

sam

e or

nex

t day

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok c

hlor

oqui

nesa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

sam

e or

nex

t day

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gssa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok S

P/Fa

nsid

ar

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

okch

loro

quin

e

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gs

% o

f pre

gnan

tw

omen

who

took

at le

ast 2

dos

esSP

/Fan

sida

r

2003

DH

S 2

003

Urb

an5

--

87

3-

224

2003

DH

S 2

003

Rur

al7

--

1212

4-

284

Libe

ria20

05M

IS 2

005

Tota

l-

--

--

--

--

Mad

agas

car

2000

MIC

S 2

000

Tota

l-

--

-1

30-

61-

2000

MIC

S 2

000

Urb

an-

--

--

--

62-

2000

MIC

S 2

000

Rur

al-

--

--

--

61-

2003

DH

S 2

003-

04To

tal

--

--

133

-34

-

2003

DH

S 2

003-

04U

rban

--

--

--

-36

-

2003

DH

S 2

003-

04R

ural

--

--

--

-34

-

Mal

awi

2000

DH

S 2

000

Tota

l-

--

-23

1-

27-

2000

DH

S 2

000

Urb

an-

--

--

--

34-

2000

DH

S 2

000

Rur

al-

--

--

--

26-

2004

DH

S 2

004

Tota

l-

--

2323

1-

2843

2004

DH

S 2

004

Urb

an-

--

378

1-

4251

2004

DH

S 2

004

Rur

al-

--

217

0-

2742

2006

MIC

S 2

006

Tota

l-

--

2020

10

2445

2006

MIC

S 2

006

Urb

an-

--

24-

--

3052

2006

MIC

S 2

006

Rur

al-

--

20-

--

2344

Mal

i20

01D

HS

200

1To

tal

--

--

-38

--

-

2001

DH

S 2

001

Urb

an-

--

--

--

--

2001

DH

S 2

001

Rur

al-

--

--

--

--

2006

DH

S 2

006

Tota

l-

--

152

22-

324

2006

DH

S 2

006

Urb

an-

--

16-

--

3510

2006

DH

S 2

006

Rur

al-

--

14-

--

312

Mau

ritan

ia20

00D

HS

200

0-01

Tota

l-

--

--

--

--

2000

DH

S 2

000-

01U

rban

--

--

--

--

-

2000

DH

S 2

000-

01R

ural

--

--

--

--

-

2003

DH

S 2

003-

04To

tal

--

-12

128

-33

-

2003

DH

S 2

003-

04U

rban

--

-9

--

-27

-

2003

DH

S 2

003-

04R

ural

--

-13

--

-38

-

Moz

ambi

que

2003

DH

S 2

003

(Nat

iona

l rep

ort)

Tota

l-

--

811

15-

15-

2003

DH

S 2

003

(Nat

iona

l rep

ort)

Urb

an-

--

6-

--

13-

2003

DH

S 2

003

(Nat

iona

l rep

ort)

Rur

al-

--

9-

--

16-

Nam

ibia

2000

DH

S 2

000

Tota

l-

--

--

14-

14-

2000

DH

S 2

000

Urb

an-

--

--

--

6-

2000

DH

S 2

000

Rur

al-

--

--

--

19-

Nig

er20

00M

ICS

200

0To

tal

--

--

048

-48

-

2000

MIC

S 2

000

Urb

an-

--

--

--

59-

2000

MIC

S 2

000

Rur

al-

--

--

--

47-

2006

CD

C-M

MP

Nat

iona

l Sur

vey

Tota

l-

--

--

--

--

2006

DH

S 2

006

Tota

l0

22-

251

29-

330

2006

DH

S 2

006

Urb

an2

29-

342

38-

451

2006

DH

S 2

006

Rur

al0

20-

230

27-

310

Nig

eria

2003

DH

S 2

003

Tota

l0

24-

250

33-

341

2003

DH

S 2

003

Urb

an0

30-

311

38-

39-

2003

DH

S 2

003

Rur

al0

22-

230

31-

32-

Rw

anda

2000

DH

S 2

000

Tota

l-

--

-1

5-

9-

2000

DH

S 2

000

Urb

an-

--

--

--

12-

2000

DH

S 2

000

Rur

al-

--

--

--

9-

2000

MIC

S 2

000

Tota

l-

--

-2

7-

13-

2000

MIC

S 2

000

Urb

an-

--

--

--

21-


Ann

ex 6

B: H

ouse

hold

sur

veys

of a

ntim

alar

ial t

reat

men

t, 20

01-2

007

WH

O re

gion

Cou

ntry

/are

aYe

arSo

urce

Subg

roup

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok S

P/ F

ansi

dar

sam

e or

nex

t day

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok c

hlor

oqui

nesa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

sam

e or

nex

t day

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gssa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok S

P/Fa

nsid

ar

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

okch

loro

quin

e

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gs

% o

f pre

gnan

tw

omen

who

took

at le

ast 2

dos

esSP

/Fan

sida

r

2000

MIC

S 2

000

Rur

al-

--

--

--

12-

2005

DH

S 2

005

Tota

l2

--

74

--

120

2005

DH

S 2

005

Urb

an1

--

72

--

111

2005

DH

S 2

005

Rur

al2

--

74

--

130

Sao

Tom

e an

d P

rinci

pe20

00M

ICS

200

0To

tal

--

--

161

-61

-

2000

MIC

S 2

000

Urb

an-

--

--

--

62-

2000

MIC

S 2

000

Rur

al-

--

--

--

61-

2006

MIC

S 2

006

Tota

l-

--

171

26

25-

2006

MIC

S 2

006

Urb

an-

--

17-

--

22-

2006

MIC

S 2

006

Rur

al-

--

16*

--

-28

*-

Sen

egal

2000

MIC

S 2

000

Tota

l-

--

-1

36-

36*

-

2000

MIC

S 2

000

Urb

an-

--

--

--

53*

-

2000

MIC

S 2

000

Rur

al-

--

--

--

30-

2005

DH

S 2

005

Tota

l1

8-

121

17-

279

2005

DH

S 2

005

Urb

an1

11-

162

23-

3411

2005

DH

S 2

005

Rur

al0

7-

100

13-

228

2006

MIS

200

6To

tal

--

-11

07

-22

49

2006

MIS

200

6U

rban

--

-12

--

-19

55

2006

MIS

200

6R

ural

--

-10

--

-24

46

Sie

rra

Leon

e20

00M

ICS

200

0To

tal

--

--

460

-61

-

2000

MIC

S 2

000

Urb

an-

--

--

--

61-

2000

MIC

S 2

000

Rur

al-

--

--

--

61-

2005

MIC

S 2

005

Tota

l-

--

451

461

522

2005

MIC

S 2

005

Urb

an-

--

49-

--

585

2005

MIC

S 2

005

Rur

al-

--

44-

--

501

Sw

azila

nd20

00M

ICS

200

0To

tal

--

--

623

-26

-

2000

MIC

S 2

000

Urb

an-

--

--

--

28-

2000

MIC

S 2

000

Rur

al-

--

--

--

27-

Togo

2000

MIC

S 2

000

Tota

l-

--

-3

59-

60-

2000

MIC

S 2

000

Urb

an-

--

--

--

62-

2000

MIC

S 2

000

Rur

al-

--

--

--

59-

2005

CD

C-M

MP

Nat

iona

l Sur

vey

Tota

l-

--

--

--

--

2006

MIC

S 2

006

Tota

l-

--

383

321

4818

2006

MIC

S 2

006

Urb

an-

--

49-

--

5718

2006

MIC

S 2

006

Rur

al-

--

32-

--

4318

Uga

nda

2000

DH

S 2

000-

01To

tal

--

--

--

--

-

2000

DH

S 2

000-

01U

rban

--

--

--

--

-

2000

DH

S 2

000-

01R

ural

--

--

--

--

-

2004

AIS

200

4-05

Tota

l-

--

--

--

--

2004

AIS

200

4-05

Urb

an-

--

--

--

--

2004

AIS

200

4-05

Rur

al-

--

--

--

--

2006

DH

S 2

006

Tota

l10

201

2919

413

6116

2006

DH

S 2

006

Urb

an2

100

276

244

5817

2006

DH

S 2

006

Rur

al3

131

296

283

6216

Uni

ted

Rep

ublic

of T

anza

nia

1999

DH

S 1

999

Tota

l-

--

--

--

53-

1999

DH

S 1

999

Urb

an-

--

--

--

62-

1999

DH

S 1

999

Rur

al-

--

--

--

52-

2004

DH

S 2

004-

05To

tal

212

551

242

258

22

2004

DH

S 2

004-

05U

rban

222

257

--

-65

29

2004

DH

S 2

004-

05R

ural

212

550

--

-57

20


Ann

ex 6

B: H

ouse

hold

sur

veys

of a

ntim

alar

ial t

reat

men

t, 20

01-2

007

WH

O re

gion

Cou

ntry

/are

aYe

arSo

urce

Subg

roup

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok S

P/ F

ansi

dar

sam

e or

nex

t day

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok c

hlor

oqui

nesa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

sam

e or

nex

t day

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gssa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok S

P/Fa

nsid

ar

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

okch

loro

quin

e

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gs

% o

f pre

gnan

tw

omen

who

took

at le

ast 2

dos

esSP

/Fan

sida

r

Zam

bia

1999

MIC

S 1

999

Tota

l-

--

-2

56-

58-

1999

MIC

S 1

999

Urb

an-

--

--

--

58-

1999

MIC

S 1

999

Rur

al-

--

--

--

58-

2001

DH

S 2

001-

02To

tal

136

-37

250

-52

-

2001

DH

S 2

001-

02U

rban

233

-36

546

-49

-

2001

DH

S 2

001-

02R

ural

136

-37

251

-53

-

2006

MIS

200

6To

tal

--

-37

33-

1358

61

2006

MIS

200

6U

rban

--

-49

--

-74

71

2006

MIS

200

6R

ural

--

-35

--

-55

56

Zim

babw

e19

99D

HS

199

9To

tal

--

--

--

--

-

2005

DH

S 2

005-

06To

tal

13

-3

14

-5

6

2005

DH

S 2

005-

06U

rban

01

-1

01

-1

3

2005

DH

S 2

005-

06R

ural

14

-5

15

-6

8

Am

eric

asC

olom

bia

2000

DH

S 2

000

Tota

l-

--

--

--

--

2000

DH

S 2

000

Urb

an-

--

--

--

--

2000

DH

S 2

000

Rur

al-

--

--

--

--

Gua

tem

ala

1999

MIC

S 1

999

Tota

l-

--

--

--

--

Hai

ti20

00D

HS

200

0To

tal

--

--

-12

-12

-

2000

DH

S 2

000

Urb

an-

--

--

--

7-

2000

DH

S 2

000

Rur

al-

--

--

--

13-

2005

DH

S 2

005

Tota

l-

--

--

4-

5-

2005

DH

S 2

005

Urb

an-

--

--

4-

7-

2005

DH

S 2

005

Rur

al-

--

--

4-

4-

Hon

dura

s20

05D

HS

200

5-06

Tota

l-

--

--

--

1-

2005

DH

S 2

005-

06U

rban

--

--

--

-1

-

2005

DH

S 2

005-

06R

ural

--

--

--

-0

-

Nic

arag

ua20

01D

HS

200

1To

tal

--

--

02

-2

-

2001

DH

S 2

001

Urb

an-

--

--

--

1-

2001

DH

S 2

001

Rur

al-

--

--

--

3-

Sur

inam

e20

00M

ICS

200

0To

tal

--

--

--

--

-

East

ern

Med

iterr

anea

nD

jibou

ti20

06M

ICS

200

6To

tal

--

-3

45

010

-

2006

MIC

S 2

006

Urb

an-

--

3-

--

10-

2006

MIC

S 2

006

Rur

al-

--

0-

--

0-

Iraq

2000

MIC

S 2

000

Tota

l-

--

-0

1-

1-

2000

MIC

S 2

000

Urb

an-

--

--

--

1-

2000

MIC

S 2

000

Rur

al-

--

--

--

2-

Som

alia

2006

MIC

S 2

006

Tota

l-

--

32

61

81

2006

MIC

S 2

006

Urb

an-

--

7-

--

141

2006

MIC

S 2

006

Rur

al-

--

1-

--

61

Sud

an20

00M

ICS

200

0To

tal

--

--

149

-50

-

2000

MIC

S 2

000

Urb

an-

--

--

--

61-

2000

MIC

S 2

000

Rur

al-

--

--

--

42-

2006

Nat

iona

l Hou

seho

ld S

urve

yTo

tal

--

--

--

--

-

Euro

peA

zerb

aija

n20

00M

ICS

200

0To

tal

--

--

-0

-1

-

2000

MIC

S 2

000

Urb

an-

--

--

--

1-

2000

MIC

S 2

000

Rur

al-

--

--

--

1-

Tajik

ista

n20

05M

ICS

200

5To

tal

--

-1

00

02

-

2005

MIC

S 2

005

Urb

an-

--

0-

--

1-

2005

MIC

S 2

005

Rur

al-

--

2-

--

2-


Ann

ex 6

B: H

ouse

hold

sur

veys

of a

ntim

alar

ial t

reat

men

t, 20

01-2

007

WH

O re

gion

Cou

ntry

/are

aYe

arSo

urce

Subg

roup

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok S

P/ F

ansi

dar

sam

e or

nex

t day

% o

f chi

ldre

n <

5w

ith fe

ver w

hoto

ok c

hlor

oqui

nesa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

sam

e or

nex

t day

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gssa

me

or n

ext d

ay

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok S

P/Fa

nsid

ar

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

okch

loro

quin

e

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok A

CT

% o

f chi

ldre

n <

5ye

ars

with

feve

rw

ho to

ok a

nyan

timal

aria

l dru

gs

% o

f pre

gnan

tw

omen

who

took

at le

ast 2

dos

esSP

/Fan

sida

r

Sout

h-Ea

st A

sia

Dem

ocra

tic R

epub

lic o

f Tim

or-L

este

2002

MIC

S 2

002

Tota

l-

--

-12

43-

47-

2002

MIC

S 2

002

Urb

an-

--

--

--

56-

2002

MIC

S 2

002

Rur

al-

--

--

--

45-

Indi

a20

00M

ICS

200

0To

tal

--

--

--

-12

-

2000

MIC

S 2

000

Urb

an-

--

--

--

14-

2000

MIC

S 2

000

Rur

al-

--

--

--

10-

2005

DH

S 2

005-

06To

tal

--

--

--

--

-

2005

DH

S 2

005-

06U

rban

--

--

--

--

-

2005

DH

S 2

005-

06R

ural

--

--

--

--

-

Indo

nesi

a20

00M

ICS

200

0To

tal

--

--

03

-4

-

2000

MIC

S 2

000

Urb

an-

--

--

--

6-

2000

MIC

S 2

000

Rur

al-

--

--

--

4-

2002

DH

S 2

002-

03To

tal

--

--

01

-1

-

2002

DH

S 2

002-

03U

rban

--

--

--

-1

-

2002

DH

S 2

002-

03R

ural

--

--

--

-1

-

Wes

tern

Pac

ific

Cam

bodi

a20

05D

HS

200

5To

tal

--

--

-0

-0

-

2005

DH

S 2

005

Urb

an-

--

--

0-

1-

2005

DH

S 2

005

Rur

al-

--

--

0-

0-

Lao

Peo

ple'

s D

emoc

ratic

Rep

ublic

2000

MIC

S 2

000

Tota

l-

--

-0

9-

9-

2000

MIC

S 2

000

Urb

an-

--

--

--

2-

2000

MIC

S 2

000

Rur

al-

--

--

--

11-

Vie

t Nam

2000

MIC

S 2

000

Tota

l-

--

-1

4-

7-

2000

MIC

S 2

000

Urb

an-

--

--

--

10-

2000

MIC

S 2

000

Rur

al-

--

--

--

6-

2005

AIS

200

5To

tal

--

--

--

--

-

2005

AIS

200

5U

rban

--

--

--

--

-

2005

AIS

200

5R

ural

--

--

--

--

-

2006

MIC

S 2

006

Tota

l-

--

22

00

3-

2006

MIC

S 2

006

Urb

an-

--

2-

--

2-

2006

MIC

S 2

006

Rur

al-

--

2-

--

3-

*Dat

a up

date

d by

DH

S s

ince

the

orig

inal

pub

licat

ion.

ªPer

cent

ages

cal

cula

ted

usin

g th

e po

pula

tion

at ri

sk.

AIS

= A

IDS

Indi

cato

r Sur

vey.

CD

C-M

MP

= C

ente

rs fo

r Dis

ease

Con

trol a

nd P

reve

ntio

n - M

alar

ia M

easl

es P

artn

ersh

ip.

DH

S =

Dem

ogra

phic

and

Hea

lth S

urve

y.

MIC

S =

Mul

tiple

Indi

cato

r Clu

ster

Sur

vey.

MIS

= M

alar

ia In

dica

tor S

urve

y.

ANNEX 7

Funding for malaria control, 2004–2007


Ann

ex 7

: Fun

ding

for m

alar

ia c

ontr

ol, 2

004-

2007

WH

O re

gion

Cou

ntry

/are

aYe

arG

loba

l Fun

daPM

IbTh

e W

orld

Ban

kcG

over

nmen

tG

loba

l Fun

dTh

e W

orld

Ban

kB

ilate

rals

UN

age

ncie

sEu

rope

anU

nion

Oth

erA

fric

aA

ngol

a20

04-

1 00

0 00

0-

--

--

--

-20

0519

510

833

1 30

0 00

0-

--

-2

100

000

1 06

2 26

6-

18 6

07 8

9520

063

203

423

7 50

0 00

0-

--

330

000

9 00

0 00

01

540

225

-4

554

259

2007

8 55

9 05

4-

--

--

--

--

Ben

in20

041

725

397

2 00

0 00

0-

1 80

0 00

02

400

000

--

1 10

0 00

0-

2 00

0 00

020

051

094

616

2 00

0 00

0-

1 25

0 00

0-

--

1 10

0 00

0-

-20

0638

7 52

71

774

000

31 0

00 0

00-

-31

000

000

-1

100

000

-5

328

000

2007

361

858

--

--

--

--

-B

otsw

ana

2004

--

-35

6 52

5-

--

--

-20

05-

--

391

131

--

--

--

Bur

kina

Fas

o20

042

298

000

--

197

387

2 92

5 51

3-

200

000

1 15

3 48

4-

207

561

2005

4 19

3 55

8-

-20

0 00

04

193

558

-21

5 24

745

7 41

1-

359

861

2006

--

12 0

00 0

001

119

648

-12

000

000

200

000

747

985

-32

5 00

020

07-

--

1 05

8 47

6-

--

117

037

--

Bur

undi

2004

4 63

1 01

750

0 00

0-

12 4

99 0

467

447

706

342

200

61 2

501

951

729

--

2005

6 26

0 39

850

0 00

0-

15 8

32 3

806

344

420

250

000

61 2

5067

5 00

030

0 00

070

000

2006

3 63

8 26

9-

-19

165

713

3 97

3 99

9-

61 2

501

815

974

-17

0 00

020

072

881

171

--

--

--

--

-C

amer

oon

2004

1 88

6 21

5-

-7

147

000

--

-19

5 00

0-

-20

055

155

782

--

7 50

4 00

012

416

102

--

195

000

--

2006

8 60

6 16

4-

-7

880

000

4 47

2 74

2-

-28

0 00

0-

-20

075

122

854

--

--

--

--

-C

entra

l Afri

can

Rep

ublic

2004

--

-90

000

--

-11

7 00

090

000

-20

051

872

782

--

100

000

6 32

9 20

1-

-11

8 00

090

000

-20

064

217

076

--

100

000

4 26

3 62

2-

-12

5 00

010

0 00

0-

2007

4 28

7 67

2-

--

--

--

--

Cha

d20

04-

--

40 0

00-

-48

7 62

663

4 00

0-

-20

05-

--

40 0

00-

-79

4 34

81

921

000

--

2006

--

-40

000

--

808

109

604

000

--

Com

oros

2004

599

483

--

--

--

1 53

4 63

1-

-20

0545

5 76

9-

-80

000

--

-96

000

--

2006

479

379

--

80 0

00-

--

--

-20

0739

0 24

6-

--

--

--

--

Côt

e d'

Ivoi

re20

04-

--

2 34

1 78

6-

-60

642

1 46

0 51

9-

-20

05-

--

--

-60

642

2 40

9 82

6-

-20

06-

--

2 42

7 23

9-

-73

8 24

53

016

659

--

2007

4 32

5 69

0-

--

--

--

--

Dem

ocra

tic R

epub

lic o

f the

Con

go20

041

441

186

2 90

0 00

0-

--

--

--

-20

0518

579

231

2 05

0 00

030

000

000

--

--

--

-20

066

471

520

--

--

--

--

-20

075

184

339

-13

000

000

--

--

--

-E

quat

oria

l Gui

nea

2006

3 48

3 90

5-

--

--

--

--

2007

1 79

9 58

3-

--

--

--

--

Erit

rea

2004

756

152

600

000

--

1 08

0 20

91

006

250

-1

455

171

-92

030

2005

1 53

7 41

847

4 36

62

000

000

-1

537

424

880

620

-49

1 00

0-

272

500

2006

1 14

0 63

5-

--

1 71

6 84

445

3 40

0-

408

613

-18

0 00

020

073

137

002

--

--

--

--

-E

thio

pia

2004

-2

000

000

--

21 7

57 6

3911

120

1 62

4 31

11

602

052

-8

444

2005

20 0

23 4

222

000

000

--

-69

5 03

768

5 73

862

0 89

4-

630

703

2006

70 0

74 8

002

563

000

21 5

00 0

00-

71 4

21 6

2715

128

000

-14

128

937

-14

639

958

2007

17 4

80 2

52-

12 2

00 0

00-

58 6

09 4

7314

699

888

-5

593

555

-21

442

875

Con

trib

utio

ns re

port

ed b

y co

untr

ies

(US$

)C

ontr

ibut

ions

repo

rted

by

dono

rs (U

S$)


Ann

ex 7

: Fun

ding

for m

alar

ia c

ontr

ol, 2

004-

2007

WH

O re

gion

Cou

ntry

/are

aYe

arG

loba

l Fun

daPM

IbTh

e W

orld

Ban

kcG

over

nmen

tG

loba

l Fun

dTh

e W

orld

Ban

kB

ilate

rals

UN

age

ncie

sEu

rope

anU

nion

Oth

er

Con

trib

utio

ns re

port

ed b

y co

untr

ies

(US$

)C

ontr

ibut

ions

repo

rted

by

dono

rs (U

S$)

Gab

on20

041

224

253

--

--

--

--

-20

053

091

210

--

--

--

--

-20

064

059

253

--

--

--

--

-20

073

063

767

--

--

--

--

-G

ambi

a20

041

456

473

--

135

570

1 45

6 47

3-

-71

0 00

0-

554

600

2005

3 77

2 42

3-

-14

5 90

03

772

423

--

400

000

-20

0 00

020

062

521

319

--

459

014

2 52

1 31

9-

-40

0 00

0-

200

000

2007

6 80

3 73

7-

--

--

--

--

Gha

na20

042

034

960

1 00

0 00

0-

--

--

--

-20

0515

370

497

1 30

0 00

0-

--

--

--

-20

065

177

461

1 47

8 00

0-

24 8

30 0

0021

762

030

-12

530

000

4 00

070

000

-20

0713

723

225

-10

000

000

-15

527

108

5 00

0 00

017

000

000

22 8

40-

13 2

33G

uine

a20

041

220

983

--

-88

5 34

9-

-23

2 46

2-

8 16

420

053

406

208

300

000

--

--

-54

397

-34

686

2006

-2 2

25 5

74-

8 10

0 00

0-

--

--

--

2007

2 83

3 47

4-

--

--

--

--

Gui

nea-

Bis

sau

2004

192

906

--

--

--

--

-20

051

076

489

--

--

--

--

-20

0620

0 00

0-

--

--

--

--

2007

677

067

--

--

--

--

-K

enya

2004

3 69

9 90

61

200

000

-1

233

505

--

5 56

3 68

483

0 80

2-

-20

05-

1 20

0 00

0-

379

494

53 6

98 9

10-

14 5

25 4

0467

2 91

1-

441

400

2006

52 1

88 9

695

470

000

-30

8 66

039

858

515

-6

236

657

30 0

00-

119

971

2007

4 94

9 79

9-

6 00

0 00

0-

--

--

--

Libe

ria20

042

797

574

300

000

--

--

--

--

2005

3 38

7 04

145

0 00

0-

27 2

165

024

741

--

--

-20

065

956

306

--

44 5

695

098

262

--

--

-M

adag

asca

r20

043

781

455

2 00

0 00

0-

-6

744

844

--

--

-20

0517

576

016

2 30

0 00

0-

-31

927

971

--

--

-20

065

834

491

2 16

9 00

0-

-12

786

722

90 0

0047

5 00

012

751

000

-29

190

207

2007

20 9

64 5

06-

--

--

--

--

Mal

awi

2004

-1

500

000

--

--

--

--

2005

-2

125

000

--

--

--

--

2006

6 36

3 50

72

045

000

5 00

0 00

012

000

000

6 30

0 00

03

000

000

-2

700

000

--

2007

11 5

94 2

07-

--

--

--

--

Mal

i20

0426

6 50

01

800

000

--

--

--

--

2005

746

721

2 42

5 00

0-

--

--

--

-20

0680

2 82

82

490

000

11 3

00 0

00-

--

--

--

2007

4 21

6 97

5-

--

--

--

--

Mau

ritan

ia20

0443

2 74

5-

--

--

--

--

2005

248

254

--

--

--

--

-20

0660

0 10

5-

11 3

00 0

00-

14-

--

--

2007

1 34

6 38

0-

--

--

--

--

Moz

ambi

que

2004

6 65

3 71

81

500

000

--

--

--

--

2005

-2

100

000

--

--

--

--

2006

5 38

0 30

66

259

000

--

--

--

--

2007

12 4

32 8

71-

--

--

--

--

Nam

ibia

2004

349

654

--

--

--

100

000

--

2005

1 37

0 77

0-

--

1 32

3 64

1-

--

--

2006

1 93

0 31

2-

--

1 13

5 78

9-

--

--


Ann

ex 7

: Fun

ding

for m

alar

ia c

ontr

ol, 2

004-

2007

WH

O re

gion

Cou

ntry

/are

aYe

arG

loba

l Fun

daPM

IbTh

e W

orld

Ban

kcG

over

nmen

tG

loba

l Fun

dTh

e W

orld

Ban

kB

ilate

rals

UN

age

ncie

sEu

rope

anU

nion

Oth

er

Con

trib

utio

ns re

port

ed b

y co

untr

ies

(US$

)C

ontr

ibut

ions

repo

rted

by

dono

rs (U

S$)

Nam

ibia

2007

6 78

9 37

5-

--

--

--

--

Nig

er20

042

882

940

--

444

231

11 2

57 9

88-

-31

3 77

5-

210

852

2005

10 2

16 6

24-

-34

2 34

6-

--

--

-20

064

914

290

-10

000

000

--

--

--

-20

072

608

119

--

--

--

--

-N

iger

ia20

048

851

119

2 40

0 00

0-

390

625

--

--

--

2005

5 78

4 64

82

900

000

-1

953

125

15 0

00 0

00-

--

--

2006

13 1

07 7

872

661

000

180

000

000

10 0

00 0

0016

000

000

2 00

0 00

0-

8-

-20

0728

022

180

--

11 0

00 0

0020

000

000

28 7

00 0

00-

--

-R

wan

da20

047

428

843

1 00

0 00

0-

--

--

--

-20

054

395

064

1 00

0 00

0-

--

--

--

-20

0632

601

503

1 47

9 00

0-

--

--

--

-20

071

391

593

--

--

--

--

-S

ao T

ome

and

Prin

cipe

2004

--

-40

1 19

1-

-1

000

000

55 8

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a Sou

rce:

The

Glo

bal F

und

web

site

(Mal

aria

spe

cific

gra

nts,

Inte

grat

ed a

nd H

ealth

Sys

tem

s S

treng

then

ing

gran

ts a

re n

ot in

clud

ed)

b Sou

rce:

PM

I web

site

c Sou

rce:

The

Wor

ld B

ank

web

site

, fun

ds fo

r 3 y

ears

*(U

S$8

mill

ion,

in ID

A c

ofin

anci

ng b

y th

e R

ussi

an F

eder

atio

n)

world malaria report 2008 · 2008. 12. 24. · world malaria report 2008 v acknowledgements the...

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